These results suggest that (1) the differences between REM and NREM can be effectively traced using simple techniques such as weak stationarity detection (**Figure 8**), and (2) there are real differences between groups, though not enough to be detected statistically by this method.

# 8. DISCUSSION

REM sleep enhances memory and attention processes by cholinergic inputs (Braun et al., 1997) via pontine (Datta et al., 2004) and basal forebrain structures (Blake and Boccia, 2017). During normal aging and especially during pathological aging, attention and memory processes become more vulnerable, and cholinergic neurons are mostly affected (Schliebs and Arendt, 2011). Aging affects various anatomic structures resulting in a loss of dendritic arbor in cortical neurons that show degradation in their structural fractal complexity (Lipsitz and Goldberger, 1992).

Non-linear dynamics and complex systems appear to be well suited to explain these phenomena (Babloyantz and Destexhe,

TABLE 6 | Scaling results for each group in the transition from NREM to REM at anteroposterior and posterior networks.

CTRL, Control group; MCI, Mild Cognitive Impairment group; REM, Rapid Eye Movement sleep; NREM, Non-REM sleep; LOG, Left Oculogram; ROG, Right Oculogram; EMG, Electromyogram. Significant results for ANOVA tests are indicated in bold. In each case, the Greenhouse-Geisser's correction for repeated measures was employed.

1986). There is a number of reports specifically addressing EEG signals during sleep: from the theoretical quantification of the effects of non-linearity (Aeschbach and Borbérly, 1993; Fell et al., 1993) to characterization of some pathologies (Röschke et al., 1995). Recent advances in nonlinear dynamics have pointed toward the relevance of using the framework of power-laws and associated tools to extract information from the EEGs (Lee et al., 2004), nevertheless, subjects under investigation have been few; only healthy subjects or subjects with sleep disorders as apnea, insomnia or narcolepsy have been studied, and non-linear methods have been heterogeneous.

Recent work showed a generalization of the DFA, named multivariate DFA (MVDFA) (Xiong and Shang, 2017). This analysis is very similar to the one developed by our group and referred to in this paper as mDFA (Rodriguez et al., 2011). Xiong and Shang (2017) showed the validity of the proposed MVDFA illustrated by numerical simulation on synthetic multivariate processes as well as on stock indices in Chinese and U.S. stock markets. Furthermore, these authors calculated the DFA of a single time series and showed that MVDFA is related to the average DFA of each time series. In this research we used mDFA to show interhemispheric, anteroposterior and posterior network behavior between EEG recordings and DFA of a single channel to validate differences between the CTRL and the MCI groups.

Lee et al. (2004) calculated the Hurst exponent of the recordings of normal sleep stages of six healthy subjects against

the Hurst exponent of six recordings of apnea from MIT/BIH polysomnography database. The scaling exponents of apnea were found to be lower than those of healthy subjects.

Acharya et al. (2005) computed several parameters, including the Hurst exponent but not through DFA. They worked out their analysis with eight EEG data from the sleep-EDF database from the PhysioBank, a data resource.

Weiss et al. (2009) based their study on the data from ten subjects, a similar number of subjects to those studied in the present research. Weiss et al. (2011) confirmed their previous results concerning Hurst values but now with twenty two participants. They added correlation analyses between Hurst exponents and the range of fractal spectra to strengthen their previous results through the use of fractal analysis to emphasize known phenomena of human sleep, in this case by proving that fractal range was a better estimating measure for classifying sleep stages.

Kumar et al. (2012) have proposed a pharmacological and neurophysiological model to reveal how the transition from wakefulness to NREM and REM sleep occurs. The transition has been explored in younger adults or in people with sleep disorders by means of mathematical analyses of one of the markers of REM sleep. In Kishi et al. (2011), a control night was compared with a night after a dose of risperidone, but only latencies and percentages of EEG stages were obtained. In another work in healthy young subjects an abrupt change in spectral analysis for two broad bands was observed on the EMG: one from 24 to 28 Hz and the other from 28 to 32 Hz (Rosales-Lagarde et al., 2009). Also, Bliwise et al. (1974) searched for EMG changes in 5 young female adults and discovered that the lowest tonic levels in EMG occurred just before REM sleep, increasing for subsequent periods of NREM sleep and decreasing again before the subsequent REM period. Hadjiyannakis et al. (1997) followed the three REM sleep markers, did spectral analysis of the EEG in a larger window than the above mentioned study of Rosales-Lagarde et al. (2009) and concluded that neither in normal

controls nor in narcoleptics, abrupt modifications in the power density of the frequencies were observed.

Nevertheless, the NREM to REM transition has not been explored by non-linear analysis with the exception of Bizzotto et al. (2010) who used Markov-chain models in insomnia patients, but to our knowledge no paper has presented a non-linear analysis in the NREM to REM transition in MCI subjects.

In our research, the three indicators of sleep were tested. There were selective differences in the transition from NREM to REM sleep for each group.

On the EEG, while the CTRL group, with preserved memory, attention and executive functions diminished its signal structure in frontotemporal areas from NREM to REM sleep, the MCI group had higher values suggestive of a tendency toward Brownian noise, strongly dominated by low frequencies, a result that had to be confirmed later using spectral analysis. In this matter, CTRL results agree with the findings of Weiss et al. (2009), because lower Hurst values were found in frontal regions in REM sleep. This is in accordance to several studies that refer to an anteroposterior gradient using metabolic techniques in resting states, indicating a greater hypofrontality relative to age (Moeller et al., 1996), but also during REM sleep. Only the left occipital area distinguished between stages. Likewise, Weiss et al. (2009) found differences for the Hurst values from NREM4 to REM in that site. The mDFA rendered a frontopolar relationship, because while the CTRL group diminished its scaling exponents, the MCI group increased them.

Inter-individual connectivity has been proved to distinguish heteromodal cortices from primary areas (Mueller et al., 2013) and in this work has been associated with group differences in cognitive functioning in the NREM and REM transition.

A dissimilar pattern among interhemispheric coupling, anteroposterior and posterior areas (Pérez-Garci et al., 2001; Corsi-Cabrera et al., 2003) was not found. Instead, the individual scaling exponents and the networks followed a greater Hurst value for the MCI group.

stationary (blue) and adopts another non-stationary pattern for REM sleep (green). LOG, Left Oculogram; ROG, Right Oculogram; EMG, Electromyogram.

From NREM to REM, at both right and left EOG, the MCI group increased its signal structure toward Brownian noise. The correlations with Hurst values of the EOGs in NREM sleep rendered a negative association with age and a positive one with education. Also, in NREM sleep, the left posterior network was

is sensitive for measuring cognitive impairment. Muscle activity at REM sleep was negatively correlated with education. Van der Hiele et al. (2011) found more theta activity, less alpha reactivity, and more frontal EMG in Alzheimer's patients than in controls. Increased EMG activity indicated more cognitive impairment and more depressive complaints. Also, Chen et al. (2011) used root mean square and frequency peak analysis and found greater values for MCI patients. In the present study, contrary to EEG and EOG results, EMG values tended to have scaling exponents of white noise, or, as stated above, anticorrelations: large values are expected to be followed by small values and vice versa.

positively correlated with Neuropsi scores, meaning this network

Regarding the great number of participants with RLS, the latter could not be diagnosed, because all MCI subjects stated their sleep was good and were practically without sleep complaints. Periodic RLS episodes are followed by increases in power, heart beat and arousals (Sieminski et al., 2017). Ferri et al. (2015) concluded that RLS is connected, but not in a simple causal relation, to arousals. The conclusion of Frauscher et al. (2014) refers to a high rate of motor events even in normal subjects. These authors found PLM especially in N1 stage, being the median of 5 per hour. The great number of arousals can help to explain group differences, because subjective complaints, both of memory and sleep, were minimal. Changes of betaamyloid ocurring in sleep disorders point toward the disruption of NREM sleep and, in particular, of SWS (Brown et al., 2016; Cellini, 2017). Potential underestimation of memory and sleep may affect these MCI subjects more than CTRLs. Gender effects could have influenced these results (Nikulin and Brismar, 2005), because almost all the CTRL group were women and most of the MCI subjects were men. In future works this factor must be controlled for.

The arbitrary exception for the degree of detection of stationarity when using the whole-night register, instead of a few NREM and REM epochs, was motivated mainly to counter the small-sample effect, but it was also preferred for being one of the fastest algorithms to compute (Nason, 2013). Previous results showed that this technique can be used for detecting

sleep stages for OAs (Rosales-Lagarde et al., 2017), so these new results indicate that such detection is still valid for some cognitive impaired OAs.

Finally, it must be highlighted that larger samples are needed to confirm the present findings.

#### 9. CONCLUSION

At the NREM to REM transition, executive functioning in MCI subjects was associated with brown noise in frontotemporal and LOG and ROG scaling exponents. On the EEG, both for DFA and mDFA, MCI OAs performing poorly on memory, attention and executive functions increased their Hurst values toward Brown noise from NREM to REM stages, while the CTRL group followed an opposite direction. On the EOG, both groups increased their Hurst values, and again the MCI group came nearer to its fractal breakdown. Muscle scaling values were lower than cerebral and eye movement Hurst values. Stationary differences were found at the whole register for the distinction of stages within the groups. Given the small size of the samples, any conclusion should be considered as preliminary and to confirm this data larger studies are needed.

#### ETHICS STATEMENT

This study was carried out in accordance with the recommendations of La Coordinación de Investigación of the Instituto de Ciencias de la Salud at the Universidad Autónoma del Estado de Hidalgo. The protocol was approved by El Comité de Ética de Investigación. All subjects gave written informed consent in accordance with the Declaration of Helsinki.

#### AUTHOR CONTRIBUTIONS

AR-L, GV-T, and CM-A contributed the neuropsychological testing and selection of the OAs. AR-L and GV-T conducted the PSGs. AR-L and LC-R scored the PSGs. AR-L wrote most of the paper related to the clinical, neuropsychological and psychophysiological sections. ER-T organized VG-M and JE-A to do the Hurst and stationarity analysis, respectively. AR-L, JE-A, and ER-T did the statistical analysis, the tables and figures. ER-T, BI-O and PM wrote the mathematical section approaches of the analysis and helped with the format and the discussion. JL-N and JP-S provided administrative and technical support to carry out the sleep measurements at the UAEH and helped with the references.

#### FUNDING

GV-T received a fellowship from the Consejo Nacional de Ciencia y Tecnología (CONACyT) number (416939). This research was accepted by the authorities of the Instituto de Ciencias de la Salud (ICSa) of the Universidad Autónoma del Estado de Hidalgo (UAEH) and belongs to the project titled "Design of tests to pre-diagnose and diagnose Elderly Adults in Hidalgo at the bio-psycho-social areas." The CONACyT, through its Cátedras CONACyT program, financed AR-L and CM-A.

#### ACKNOWLEDGMENTS

This work forms part of a master's degree thesis submitted to the Master in Biomedical Sciences and Health Program at the Instituto de Ciencias de la Salud of the UAEH (GT-V). It is also associated with a Bachelor's program in Applied Mathematics at the Instituto de Ciencias Básicas e Ingeniería of the UAEH (VG-M and JE-A). We are grateful to Patricia Padilla-Muñoz,

#### REFERENCES

coordinator of the Centro Gerontológico Integral at Punta Azul, Pachuca de Soto for her support. Also, to Geovanni de la Cruz, Minerva Granillo and Mauricio Islas for their technical aid in the registers of the polysomnographies, and finally to Patricia Pliego-Pastrana for her help in gathering information on OAs. Christopher Follett reviewed the English language version.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg. 2018.01205/full#supplementary-material

Priestley, M. B. (1981). Spectral Analysis and Time Series. London: Academic Press.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Rosales-Lagarde, Rodriguez-Torres, Itzá-Ortiz, Miramontes, Vázquez-Tagle, Enciso-Alva, García-Muñoz, Cubero-Rego, Pineda-Sánchez, Martínez-Alcalá and Lopez-Noguerola. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Effects of Mild Cognitive Impairment on the Event-Related Brain Potential Components Elicited in Executive Control Tasks

Montserrat Zurrón1 \*, Mónica Lindín1 , Jesús Cespón2 , Susana Cid-Fernández1,3 , Santiago Galdo-Álvarez1 , Marta Ramos-Goicoa1 and Fernando Díaz1

1 Laboratorio de Neurociencia Cognitiva Aplicada, Departamento de Psicoloxía Clínica e Psicobioloxía, Facultade de Psicoloxía, Universidade de Santiago de Compostela, Santiago de Compostela, Spain, 2 Basque Center on Cognition, Brain and Language, San Sebastián, Spain, 3 Sezione di Neuroscienze Cognitive – Laboratorio di Neurofisiologia, IRCCS Centro San Giovanni di Dio, Fatebenefratelli, Brescia, Italy

#### Edited by:

Antonino Vallesi, Università degli Studi di Padova, Italy

#### Reviewed by:

Rolf Verleger, Universität zu Lübeck, Germany Iris Wiegand, Max-Planck-Institut für Bildungsforschung, Germany

> \*Correspondence: Montserrat Zurrón<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Cognition, a section of the journal Frontiers in Psychology

Received: 09 March 2018 Accepted: 11 May 2018 Published: 29 May 2018

#### Citation:

Zurrón M, Lindín M, Cespón J, Cid-Fernández S, Galdo-Álvarez S, Ramos-Goicoa M and Díaz F (2018) Effects of Mild Cognitive Impairment on the Event-Related Brain Potential Components Elicited in Executive Control Tasks. Front. Psychol. 9:842. doi: 10.3389/fpsyg.2018.00842 We summarize here the findings of several studies in which we analyzed the eventrelated brain potentials (ERPs) elicited in participants with mild cognitive impairment (MCI) and in healthy controls during performance of executive tasks. The objective of these studies was to investigate the neural functioning associated with executive processes in MCI. With this aim, we recorded the brain electrical activity generated in response to stimuli in three executive control tasks (Stroop, Simon, and Go/NoGo) adapted for use with the ERP technique. We found that the latencies of the ERP components associated with the evaluation and categorization of the stimuli were longer in participants with amnestic MCI than in the paired controls, particularly those with multiple-domain amnestic MCI, and that the allocation of neural resources for attending to the stimuli was weaker in participants with amnestic MCI. The MCI participants also showed deficient functioning of the response selection and preparation processes demanded by each task.

Keywords: mild cognitive impairment, event-related brain potentials, Stroop task, Simon task, Go/NoGo task

# INTRODUCTION

Here we report the findings of several studies carried out by our research group in which we recorded event-related brain potentials (ERPs) elicited in participants with mild cognitive impairment (MCI) during performance of executive tasks. The aim of this research was to search for neurofunctional indexes of executive processes which can be used in the diagnosis of MCI. This might contribute to the early detection of Alzheimer's disease (AD), of enormous socio-sanitary importance, and to halting progression of the disease by the timely application of appropriate treatments.

In section "Executive Functions, Tasks, Behavior and Brain Electrical Activity," we describe the three executive tasks, i.e., the Stroop, Simon, and Go/No-Go tasks, which we have adapted to induce ERPs. We also describe the characteristic ERP components elicited in these tasks and the cognitive processes associated with each. In section "Aging, Mild Cognitive Impairment (MCI), Performance

of Executive Tasks and ERPs," we define the different subtypes of MCI and summarize the results obtained in our studies on the effect of MCI on the ERP components in the three tasks.

# EXECUTIVE FUNCTIONS, TASKS, BEHAVIOR AND BRAIN ELECTRICAL ACTIVITY

The term executive function encompasses those brain functions that enable us to set goals and control the skills and behaviors required to achieve these goals. These functions include a series of cognitive processes, including attention, cognitive control, working memory, cognitive inhibition, and flexibility, which we use in our activities of daily living to monitor behaviors and implement goal-directed actions (Chan et al., 2008; Diamond, 2013).

The study of brain functions that support executive processes has traditionally been carried out by analyzing the behavioral responses to classic executive tasks in patients with some type of brain lesion or alteration. More recently, functional neuroimaging techniques have been used to record the haemodynamic activity in such patients and healthy controls while they perform executive tasks (see Gilbert and Burgess, 2008; Verdejo-García and Bechara, 2010; Lezak et al., 2012). These studies have highlighted the role of prefrontal areas in executive functioning, but do not provide any information about the temporal course of brain activity associated with executive processes. The ERP technique is a valuable tool in this respect, as it enables direct measurement of brain functioning, with a high temporal resolution (milliseconds), is non-invasive (ERPs are recorded with electrodes attached to the intact scalp), simple to use and relatively inexpensive. ERPs are positive and negative deflections of voltage that are considered components of brain activity and provide information about the temporal dynamics of the different stages of stimulus processing (perception, evaluation, categorization) and response processing (selection, preparation, and control) (Luck, 2005).

Event-related brain potential components can be characterized according to their amplitude (in microvolts, µV), latency (in milliseconds, ms) and topographical distribution. Various executive tasks have been adapted for use during the recording of brain electrical activity, in order to identify the ERP components associated with executive functioning. We have adapted three such tasks for this purpose: the Stroop, Simon, and Go/NoGo tasks.

All three tasks allow the study of attentional control, as subjects must direct their attention toward the characteristics of the stimulus that are relevant to the task demands and inhibit responses that are incompatible with the demands. Thus, the N2 and P300 components have been identified in relation to stimulus processing. These components are associated with the processes of directing and controlling attention to relevant stimuli: N2/N2b is associated with evaluation (Ritter et al., 1979; Folstein and Van Petten, 2008) and P300/P3b with categorization (Donchin, 1981; Polich, 2007) of the stimuli, although these components have specific characteristics in each task. Regarding the response processing, the lateralized readiness potential (LRP) is obtained in all three tasks and is generated within the motor cortex. The LRP provides information about the timing of response selection (stimuluslocked LRP; sLRP) and of planning and executing the response (response-locked LRP; rLRP) (Smulders and Miller, 2012).

# Stroop Task

All of the different versions of the Stroop task available compare the performance (reaction times -RT- and hits) under two conditions (**Figure 1**): one in which there is conflict between two types of information, one relevant and the other irrelevant to the task (e.g., the word "red" displayed in blue -incongruent stimulus-), and another that does not produce conflict (e.g., the word "red" displayed in red -congruent stimulus- or colored X-strings). Participants are usually asked to respond to the color of the stimulus and to ignore the word, thus generating the so-called Stroop effect, whereby the RT is longer in the conflict condition than in no-conflict condition. Considering that words induce an automatic reading response, the Stroop effect is attributed to interference generated by the meaning of the incongruent word in the task of responding to the color of the stimulus (see Stroop, 1935; MacLeod, 1991).

The ERP waveforms recorded in young and old healthy participants while they are processing color-word stimuli basically include (**Figure 2**) the frontal-central N2 and the parietal P3b (Zurrón et al., 2009, 2013, 2014).

The frontal-central N2 component is characteristic of tasks in which a conflict must be resolved before the response is given (N2 is larger for higher conflict) and it has therefore been associated with cognitive control processes (see review by Folstein and Van Petten, 2008).

The parietal P3b component (or P300) is observed in attentional tasks when an informative task-relevant stimulus is detected, and the P3b amplitude is thus larger for target than non-target stimuli (Donchin, 1981; Polich, 2007).

The cognitive processes associated with N2 and P3b involve respectively evaluation and categorization of the stimulus. Indeed, N2 and P3b latencies are considered indicators of the time required to evaluate and categorize the stimulus for resolution of a task (Kutas et al., 1977; Hillyard and Kutas, 1983; Folstein and Van Petten, 2008). The P3b amplitude is smaller for incongruent stimuli than for congruent stimuli in young participants, and this difference has been considered an indicator of the semantic conflict that occurs in the former relative to the absence of such conflict in the latter (Zurrón et al., 2009).

# Simon Task

In the Simon task (**Figure 1**), participants are required to respond to a non-spatial feature (i.e., color, shape) of a lateralized stimulus by pressing one of two response buttons that are lateralized in the same spatial arrangement. Although the stimulus position is irrelevant to the task, the RT is longer when the response

side is spatially incompatible with the stimulus position than in trials that require an ipsilateral response regarding the stimulus position. This spatial interference, known as the Simon effect, is evoked by visual (Craft and Simon, 1970), auditory (Simon and Small, 1969), and somatosensory (Hasbroucq and Guiard, 1992) stimulation, regardless of whether the participants respond with hand, feet or eye movements (Leuthold and Schröter, 2006).

This paradigm enables us to study the spatial attention given to the target and to inhibition of the non-target stimulus, the cognitive control used to suppress the spatial tendency of the response and the motor processes involved in the response preparation. ERPs can be used to study these cognitive processes (**Figure 2**). The contralateral posterior negativity (N2pc), a correlate of visuospatial attention to the lateralized target stimulus and suppression of the non-target stimulus (Luck and Hillyard, 1994; Eimer, 1996), arises from extrastriate visual areas (Luck et al., 1997; Hopf et al., 2000). The contralateral central negativity (N2cc) is generated in the dorsal premotor cortex during activity involved in preventing spatial responses (Praamstra and Oostenveld, 2003; Praamstra, 2006; Cespón et al., 2012). N2pc and N2cc appear between 200 and 300 ms post-stimulus in young adults. The rLRP component is associated with response preparation.

# Go/NoGo Task

In the Go/NoGo task (**Figure 1**), participants are usually required to respond by pressing a button in response to frequent stimuli (Go stimuli), while withholding the response to different infrequent stimuli (NoGo stimuli). This task is considered an executive function task (Rubia et al., 2001), and it can be used to study several cognitive processes such as stimulus evaluation, response inhibition and response control (Botvinick et al., 2001; Lucci et al., 2013).

The ERP correlates of the Go stimuli evaluation processes are N2b and P3b, although they are often called Go-N2 and Go-P3 when obtained with this task (**Figure 2**). In young adults, N2b (latency: 200–300 ms post-stimulus) shows maximum amplitude at central electrodes and P3b (300–500 ms post-stimulus) at parietal electrodes. In addition, the sLRP and rLRP components have been studied in relation to response processing in the Go condition.

With NoGo stimuli (**Figure 2**), the NoGo-N2 and NoGo-P3 ERP components are identified at frontocentral

locations around 200–400 and 300–500 ms post-stimulus, respectively (Pfefferbaum and Ford, 1988; Jodo and Kayama, 1992; Falkenstein et al., 2002; Vallesi et al., 2009). Both components have traditionally been considered correlates of response inhibition (e.g., Jackson et al., 1999; Bokura et al., 2001; Nakata et al., 2009).

# AGING, MILD COGNITIVE IMPAIRMENT (MCI), PERFORMANCE OF EXECUTIVE TASKS AND ERPs

Executive functioning is known to decline in healthy cognitive aging (Park and Schwarz, 2002; Raz et al., 2005; Reuter-Lorenz and Park, 2010; Grady, 2012; Harada et al., 2013; Kirova et al., 2015). In Stroop tasks, the RT is slower and the Stroop effect is larger in older than in young people (see MacLeod, 1991; Van der Elst et al., 2006; Peña-Casanova et al., 2009); the Simon effect increases in normal aging (Proctor et al., 2005; Juncos-Rabadán et al., 2008), and an age-related decline in behavioral responses has been observed in Go/NoGo tasks (Falkenstein et al., 2002; Lucci et al., 2013; Correa-Jaraba et al., 2016; Hsieh et al., 2016).

The ERPs elicited in the three tasks are sensitive to cognitive decline in healthy aging, and, relative to younger participants, healthy older participants display longer N2 and P3b latencies and smaller parietal P3b amplitude in Stroop (Zurrón et al., 2014) and Go/NoGo tasks (Falkenstein et al., 2002; Vallesi, 2011), as found in oddball tasks (Polich, 1997); longer N2pc in the Simon task (Van der Lubbe and Verleger, 2002; Cespón et al., 2013a), as also observed in visual search or selection tasks (Lorenzo-López et al., 2008, 2011; Amenedo et al., 2012; Wiegand et al., 2013, 2015); and delayed rLRP onset and larger sLRP amplitude in the Simon task (Cespón et al., 2013a), in consonance with previous findings (Yordanova et al., 2004; Kolev et al., 2006; Roggeveen et al., 2007; Wild-Wall et al., 2008; Wiegand et al., 2013). In addition, the P3b distribution is frontal in older and parietal in younger participants in the Stroop task (Zurrón et al., 2014), and the central NoGo-P3 amplitude is larger in older than in younger participants (Vallesi, 2011).

Some middle-aged and old adults show greater cognitive decline than expected according to their age and educational level, although their independence in daily life activities is preserved and they do not meet the criteria for diagnosis of dementia. This intermediate stage between normal cognitive aging and dementia is denominated MCI (Petersen et al., 1999; Knopman and Petersen, 2014). Interest in MCI has increased in recent years because the condition is associated with a higher risk of progressing to dementia (Winblad et al., 2004). MCI is frequently (although not always) associated with a decline in memory functioning, which may also be accompanied by deterioration in other cognitive functions, including executive functions. Four subtypes of MCI are currently distinguished (Petersen, 2016): single-domain amnestic MCI (sdaMCI, characterized by memory impairment only), multiple-domain amnestic MCI (mdaMCI, characterized by impairment in memory and in other cognitive domains), singledomain non-amnestic MCI (sdnaMCI, preserved memory but an overt decline in another cognitive domain), and multiple-domain non-amnestic MCI (mdnaMCI, preserved memory but with evidence of decline in several cognitive domains). Individuals with amnestic MCI (aMCI), especially those with mdaMCI, display a greater risk of developing AD than the healthy old population (Petersen et al., 1999, 2009).

In our ERP studies involving administration of Stroop, Simon and Go/No-Go tasks to participants older than 50 years (aMCI participants and healthy controls matched for age and years of schooling), we found that the effects of aMCI on the behavioral responses depended on the task used (Cespón et al., 2013b, 2015a; Cid-Fernández et al., 2014, 2017a,b; Ramos-Goicoa et al., 2016). However, aMCI affected the latencies and amplitudes of ERP components associated with executive functioning in all three tasks (**Figure 2**).

In the Stroop task, aMCI did not affect behavioral responses, probably because the task was simpler (only two types of response) than the standard versions of the Stroop test. In contrast, the latency of P3b component generated by congruent and incongruent stimuli was longer in middle-aged aMCI participants than in the paired controls; however, no differences between groups were observed in response to colored X-strings, which were presented separately and did not mobilize executive processes. aMCI affected the P3b amplitude: we observed a greater difference in P3b amplitudes between conditions (colored X-strings versus congruent or incongruent stimuli) in aMCI participants than in healthy controls. As only the color of the X-string stimuli, but both the color and semantic meaning of the congruent and incongruent stimuli must be evaluated, this finding may be attributed to a greater effect of readingrelated interference in aMCI participants than in the controls. Finally, the sLRP and rLRP amplitudes were smaller in aMCI participants than in healthy controls (Ramos-Goicoa et al., 2016).

In the Simon task, the error rate (but not the RT) was affected by mdaMCI, indicating greater interference due to the spatial position of the stimulus in participants with mdaMCI than in healthy controls. In relation to stimulus processing, we observed that the latencies of the N2 and N2cc components were longer in the mdaMCI participants than in the paired controls and attributed these findings to respectively a longer time devoted to stimulus evaluation processes and delayed allocation of neural activity associated with cognitive control of the spatial response (Cespón et al., 2013b, 2015a,b). The N2pc amplitude was smaller in mdaMCI participants than in the controls, and as N2pc is an ERP correlate of the direction of the spatial attention to lateralized stimuli (Luck and Hillyard, 1994; Woodman and Luck, 1999; Hickey et al., 2009), we conclude that the mdaMCI participants show a deficit in neural resources allocated to spatial attention.

In ERPs related to response processing in the Simon task (Cespón et al., 2013b, 2015b), the rLRP amplitude was smaller in sdaMCI and mdaMCI participants than in controls, and the amplitude of the frontocentral pre-response positivity component between 80 and 20 ms pre-response (Nessler et al., 2007) was smaller in mdaMCI and sdaMCI participants than in controls.

In the Go/NoGo task, MCI participants (mainly mdaMCI) showed slower responses and fewer hits in response to Go stimuli than the controls.

The latency of the N2-Go component was longer in mdaMCI participants than in controls (Cid-Fernández et al., 2017a). This finding is consistent with those reported by Mudar et al. (2016) for a semantic-type Go/NoGo task, in which the latencies of the Go-N2 and NoGo-N2 components were longer in aMCI participants than in controls. We also observed smaller Go-N2 amplitude in sdaMCI participants than in the paired controls, as well as a smaller NoGo-N2 amplitude in sdaMCI and mdaMCI participants than in the controls. López Zunini et al. (2016) observed smaller Go-P3 and NoGo-P3 amplitudes in aMCI participants than in controls. Therefore, aMCI participants display a deficit in neural resources dedicated to the Go and NoGo stimuli. These deficit in neural resources may be unspecific, as they affect both stimuli, or they may be related respectively to evaluating and categorizing the target stimulus and to inhibiting predominant responses not relevant to the task demands.

A late positive slow wave (PSW) was identified in the 700–1000 ms interval after the Go stimulus in sdaMCI participants, and it was absent in healthy controls and subjects with mdaMCI (Cid-Fernández et al., 2017a). This ERP component overlaps with the Go-P3 and is thought to indicate additional operations after categorization of target stimuli. In the sdaMCI participants, this component may indicate compensatory operations in order to maintain acceptable levels of performance, as the behavioral response of this subgroup of participants was similar to that of the controls. Furthermore, this hypothesis was consistent with the longer sLRP latency in response to Go stimuli in sdaMCI participants than in the controls (Cid-Fernández et al., 2017b). In this study, we also observed that the amplitude of the sLRP component was smaller in mdaMCI participants than in healthy controls.

In summary, our findings on the components of the ERPs elicited in executive tasks indicate that, relative to age-matched controls, aMCI participants exhibit (a) longer

#### REFERENCES

latencies, indicating slower neural functioning in the stimulus and response processing, (b) changes in the amplitudes of the N2 and P3b components, reflecting fewer neural resources allocated for attending to the relevant dimension of the stimulus, and (c) smaller LRP amplitudes, indicating a lower capacity of the motor cortex to allocate neural resources for selection and preparation of the motor response. The decline in processing associated with aMCI is greater than in healthy aging and is more evident in mdaMCI than in sdaMCI participants.

In light of the above, we conclude that aMCI participants present neurofunctional alterations in executive functioning, independently of any behavioral responses, and we believe that the neurofunctional deficits appear prior to behavioral deficits in aMCI participants. Exploration of the sensitivity and specificity of the ERPs components is therefore of interest regarding the potential use of these components as markers of MCI and possible predictors of AD.

# AUTHOR CONTRIBUTIONS

MZ, ML, JC, SC-F, SG-Á, and FD: wrote the manuscript. JC, MR-G, and SC-F: recorded the brain electrical activity. MZ, ML, JC, SC, SG-Á, MR-G, and FD: analyzed the ERP waveforms and identified the different components. MZ, JC, SC-F, SG-Á, and MR-G: carried out statistical analysis of the data.

# FUNDING

This study was financially supported by funds from the Spanish Government: Ministerio de Economía y Competitividad (PSI2014-55316-C3-3-R); and by the Galician Government: Consellería de Cultura, Educación e Ordenación Universitaria; Axudas para a Consolidación e Estruturación de Unidades de Investigación Competitivas do Sistema Universitario de Galicia: GRC (GI-1807-USC); Ref: ED431-2017/27.

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Zurrón, Lindín, Cespón, Cid-Fernández, Galdo-Álvarez, Ramos-Goicoa and Díaz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Working Memory Deficits After Lesions Involving the Supplementary Motor Area

Alba Cañas 1 , Montserrat Juncadella1 , Ruth Lau2 , Andreu Gabarrós 2,3 and Mireia Hernández 3,4,5 \*

1 Department of Neurology, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain, 2 Department of Neurosurgery, Hospital Universitari de Bellvitge, L'Hospitalet de Llobregat, Spain, 3 Cognition and Brain Plasticity Group, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Spain, 4 Section of Cognitive Processes, Department of Cognition, Development and Educational Psychology, University of Barcelona, Barcelona, Spain, 5 Basque Center on Cognition, Brain and Language, Donostia, Spain

The Supplementary Motor Area (SMA)—located in the superior and medial aspects of the superior frontal gyrus—is a preferential site of certain brain tumors and arteriovenous malformations, which often provoke the so-called SMA syndrome. The bulk of the literature studying this syndrome has focused on two of its most apparent symptoms: contralateral motor and speech deficits. Surprisingly, little attention has been given to working memory (WM) even though neuroimaging studies have implicated the SMA in this cognitive process. Given its relevance for higher-order functions, our main goal was to examine whether WM is compromised in SMA lesions. We also asked whether WM deficits might be reducible to processing speed (PS) difficulties. Given the connectivity of the SMA with prefrontal regions related to executive control (EC), as a secondary goal we examined whether SMA lesions also hampered EC. To this end, we tested 12 patients with lesions involving the left (i.e., the dominant) SMA. We also tested 12 healthy controls matched with patients for socio-demographic variables. To ensure that the results of this study can be easily transferred and implemented in clinical practice, we used widely-known clinical neuropsychological tests: WM and PS were measured with their respective Wechsler Adult Intelligence Scale indexes, and EC was tested with phonemic and semantic verbal fluency tasks. Non-parametric statistical methods revealed that patients showed deficits in the executive component of WM: they were able to sustain information temporarily but not to mentally manipulate this information. Such WM deficits were not subject to patients' marginal PS impairment. Patients also showed reduced phonemic fluency, which disappeared after controlling for the influence of WM. This observation suggests that SMA damage does not seem to affect cognitive processes engaged by verbal fluency other than WM. In conclusion, WM impairment needs to be considered as part of the SMA syndrome. These findings represent the first evidence about the cognitive consequences (other than language) of damage to the SMA. Further research is needed to establish a more specific profile of WM impairment in SMA patients and determine the consequences of SMA damage for other cognitive functions.

Keywords: executive control, neuropsychology, neurosurgery, processing speed, SMA syndrome, supplementary motor area, verbal fluency, working memory

#### Edited by:

Gail Robinson, The University of Queensland, Australia

#### Reviewed by:

Nazbanou Nozari, Johns Hopkins University, United States Evy Visch-brink, Erasmus University Rotterdam, Netherlands

> \*Correspondence: Mireia Hernández<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Cognition, a section of the journal Frontiers in Psychology

Received: 07 January 2018 Accepted: 30 April 2018 Published: 23 May 2018

#### Citation:

Cañas A, Juncadella M, Lau R, Gabarrós A and Hernández M (2018) Working Memory Deficits After Lesions Involving the Supplementary Motor Area. Front. Psychol. 9:765. doi: 10.3389/fpsyg.2018.00765

# INTRODUCTION

The supplementary motor area (SMA) is situated in the superior and medial aspects of the superior frontal gyrus (Penfield and Welch, 1951), in front of the primary motor cortex (M1) and bordering inferiorly with the portion of the cingulate gyrus just above the genu of the corpus callosum (Talairach and Bancaud, 1966) (**Figure 1**). At a functional level, the SMA is fundamental in the selection, preparation, initiation, and execution of complex sequences of voluntary movements (Weilke et al., 2001). Such an important motor role relies on white-matter connectivity between the SMA and different core motor structures of the nervous system such as M1 (Catani et al., 2012), the striate body (Alexander and Crutcher, 1990; Lehéricy et al., 2004), or the spinal cord (Rizzolatti et al., 1996). The dominant SMA also plays a critical role in the control of motor aspects of speech production (Penfield and Rasmussen, 1950; Chassagnon et al., 2008), thanks to its connectivity with Broca's area (Vergani et al., 2014; Sierpowska et al., 2015).

The SMA happens to be a preferential site of different neurological disorders and abnormalities—most frequently tumors (especially low-grade gliomas; Duffau and Capelle, 2004) and epileptic foci (Chassagnon et al., 2008)—but cases with other etiology such as arteriovenous malformations (MAVs; Sailor et al., 2003) and cerebrovascular accidents (CVA; Dick et al., 1986; Ziegler et al., 1997; Pai, 1999) have also been described. This particularity has driven a special interest in investigating the functional symptoms derived from damage to the SMA, the collection of which is known as SMA syndrome (Laplane et al., 1977). The most apparent and common symptoms of the SMA syndrome consist of movement disorders, which are subject to the SMA somatotopy: the representation of the face, the contralateral upper limb, and the contralateral lower limb are located from middle to posterior SMA portions (Fontaine

et al., 2002). Extreme cases show contralateral hemiparesis and hemiplegia, but patients most frequently show difficulties with fine hand movements, rapid alternating sequences, and bimanual coordination (Laplane et al., 1977; Rostomily et al., 1991; Zentner et al., 1996; Pai, 1999; Bannur and Rajshekhar, 2000). As the anterior portion of the SMA in the dominant hemisphere represents language (Fontaine et al., 2002), patients may also show language disorders if the lesion is located in the dominant SMA. These language disorders consist in varying degrees of transcortical motor aphasia (Masdeu et al., 1978; Alexander and Schmitt, 1980; Berthier et al., 1991; Ziegler et al., 1997): difficulties in the initiation of speech, reduced fluency (even mutism in extreme cases), relatively preserved repetition and reading skills, and comprehension difficulties with complex language content and speech at a high speed rate.

Many SMA lesions have surgical treatment, which results in several patients recovering the altered functions. Even so, 19.46% of patients show permanent motor and speech sequelae after surgery, often interfering with daily life (Gabarrós et al., 2011).  One of the most important findings in this respect has been that the probability of permanent motor deficits increases with the severity of such deficits before surgery (Gabarrós et al., 2011). Another important contribution of those studies has been the observation that awake mapping surgery significantly reduces motor sequelae (Gabarrós et al., 2011): it allows the exact identification of the SMA by having the patient conduct a motor task (generally, either a finger-opposition motor task or a bimanual coordination one) while using electrical stimulation that, when applied to the SMA, disrupts task performance. This exact identification helps avoid unnecessary damage to SMA's healthy portions during lesion resection and, in turn, better preserve the motor function. The identification of language areas of the SMA during awake mapping surgery has also been very effective in preserving speech (Duffau et al., 2003; Sierpowska et al., 2015): electric stimulation in areas relevant for language produces speech impairment during word-generation tasks, which allows mapping of those SMA areas to protect their language function during lesion resection. These findings have represented precious information for the development of SMA protocols. The refinement of such protocols is still a priority in clinical research, as they are critical to achieving the highest effectiveness in guiding the treatment plans of SMA lesions.

In contrast to the great amount of attention devoted to motor and speech disorders, the assessment of cognitive functions (other than language) has been neglected in SMA patients. Especially surprising is the lack of attention that has been given to working memory (WM): a cognitive mechanism whose attentional component (att-WM) allows sustaining information temporally so that its executive component (ex-WM) can manipulate such information and operate with it (Baddeley, 1992, 2003; Wingfield, 2016). In fact, many neuroimaging studies with both healthy and brain-damaged individuals have evidenced that the SMA is part of a widespread fronto-parietal network underlying WM [see Owen et al.'s (2005) and Rottschy et al.'s (2012) meta-analyses]. Most of those studies used classical WM tasks in cognitive research which especially challenge the ex-WM, such as the n-back task: the participant needs to indicate whether the current stimulus matches the one from n steps earlier in a continuous sequence, with the load factor n increasing to make the task progressively more difficult in terms of load. This is particularly relevant because ex-WM is essential for higherorder functions such as reasoning, problem solving, and learning (Engle et al., 1999; Shah and Miyake, 1999). This means that ex-WM deficits could compromise an individual's capacity to perform a wide range of complex cognitive tasks. Therefore, it is fundamental to determine whether SMA damage leads to WM impairment. A positive answer to this question would mean that WM deficits need to be considered as part of the SMA syndrome and, hence, taken into account in future refinements of clinical protocols.

Nakajima et al. (2014) conducted the only prior study investigating the effects of SMA damage on WM with the description of two patients with brain tumors situated in the SMA. Using a 2-back task during awake mapping surgery, these authors obtained direct evidence that the SMA plays a role in WM, which is consistent with prior neuroimaging studies (Owen et al., 2005; Rottschy et al., 2012) that identified the SMA as one more region comprised in the WM fronto-parietal network. However, it is precisely because of the fact that WM relies on such a widespread network that it remains unclear whether permanent SMA damage would be relevant in patients' daily life. In other words, the rest of the network might overcome the permanent SMA dysfunction. Whether or not SMA damage has a real hampering effect on patients' WM can only be tested with data obtained outside the operating room. In this respect, Nakajima et al.'s (2014) data is uninformative because they merely reported the WM scores obtained by the two cases in the absence of a control group.

The main goal of the current study was to determine whether lesions involving the dominant SMA hamper WM. We focused on the dominant side because the SMA shows a certain degree of hemispheric dominance and, thus, more severe deficits are expected from damage in the dominant SMA compared to the non-dominant one (Rogers et al., 2004). With this main purpose, we conducted a group study with SMA patients and healthy controls.

We approached this study from an applied perspective using tasks from clinical practice, as opposed to experimental tasks typically used in basic cognitive research. In particular, we measured WM with the WM Index of the WAIS-III (Wechsler, 1997), which combines the scores of 3 tasks: Digit span (Dspan), Letter-Number Sequencing (LNS), and Arithmetic. The Dspan tests the capacity of mentally maintaining a series of items (digits) in the same order they were memorized. Although the second part of this task also requires the mental manipulation of those items (i.e., retrieving them in a backward order), such manipulation is rather simple. Therefore, this task fundamentally measures att-WM. The LNS is much more demanding on ex-WM, as it requires a more complex mental manipulation of a series of items: numbers and letters are mixed and the participant is required to retrieve them in a specific order different from that in which they were memorized. Arithmetic is also particularly demanding on ex-WM, but the fact that the participant needs to mentally solve mathematical problems makes it much more complex than LNS.

We also included the Processing Speed (PS) Index of the WAIS-III in the testing to assess the possibility that WM deficits in SMA patients are reducible to PS difficulties (Fry and Hale, 1996): it has been observed that brain damage tends to reduce an individual's PS (see the WAIS-IV Technical and Interpretative Manual, Wechsler, 2008; see also Hawkins, 1998; Fisher et al., 2000).  In cases of remarkably slow PS, the first processed items may no longer be available when the last ones reach WM, making it impossible to perform any manipulation or operation with them.

As a secondary goal, we set out to examine whether cognitive deficits associated to SMA damage would go beyond WM and extend to another cognitive process that is also relevant for complex cognition: executive control (EC), understood as a set of cognitive processes that are needed for the flexible allocation of mental resources in the service of goal-directed behavior (Posner and Snyder, 1975; Miller, 2000; Badre, 2008; Kouneiher et al., 2009; Solomon et al., 2009). This additional question was based on the fact that the SMA is linked to the dorsolateral prefrontal cortex (DLPFC)—a relevant brain structure for EC—through white matter connectivity (Nachev et al., 2008). We addressed this question by comparing SMA patients and controls in the performance of a task that has been often used as a broad, quick EC measure in clinical contexts: verbal fluency (Shao et al., 2014). In particular, we used a semantic (animals) and a phonemic (letter P) verbal fluency: the participant is given a limited amount of time to retrieve all animals or words starting with the letter P as she can. In addressing this question, however, it is necessary to take into consideration that WM and EC processes are tightly related. For example, some EC processes, such as the ability to suppress interference from distracting stimuli, depend on an individual's WM capacity (Redick and Engle, 2006). Such an association has also been observed with the verbal fluency task. Indeed, verbal fluency performance has been associated with WM capacity in different studies, with both healthy individuals (Rosen and Engle, 1997; Rende et al., 2002; Azuma, 2004; Hedden et al., 2005; Unsworth et al., 2010) and neuropsychological patients (Sands et al., 2000; Witt et al., 2004; Lam et al., 2006; Larsson et al., 2008; Zahodne et al., 2008). Therefore, we controlled for byproduct effects of WM when assessing the effects of SMA damage on verbal fluency.

# MATERIAL AND METHODS

# Participants

Twenty-four participants took part in this study, half of whom were patients with a lesion involving the dominant SMA and the other half were control participants. The patient and control groups were matched for gender (7 men and 5 women per group), age (patients: mean = 41.25 years, SD = 10.9; controls: mean = 41.33 years, SD = 10.54) and years of educational attainment (patients: mean = 11.5, SD = 2.7; controls: mean = 12.33, SD = 2.67). This matching was conducted by pairing each patient with a control in all those three socio-demographic variables. All participants were righthanded. Patients were attended in the Neurosurgery Service of the Hospital Universitari de Bellvitge (HUB, Barcelona, Spain) between 2012 and 2015. The etiology of the lesion was a tumor between grade I and III in all cases except for a cerebral abscess and two AVMs. All lesions were located in the left cerebral hemisphere. All patients but one underwent lesion resection. As WM and EC were assessed with verbal tasks, patients diagnosed with language impairment by their clinical neuropsychologist were not included in the study. The absence of language impairment was determined by asking patients to describe the Cookie Theft Picture of the Boston Diagnostic Aphasia Examination (Goodglass and Kaplan, 1983), with which the neuropsychologist determined that the spontaneous speech of all patients was fluent, with information content, and free of anomia and phonological, semantic, morphological, or syntactic errors. **Table 1** summarizes patients' socio-demographic characteristics and their type of SMA lesion (see also **Figure 2** for an example of an SMA lesion before and after surgical treatment).

#### Procedure

In the case of patients, experimental testing was included in the same clinical neuropsychology sessions, consisting in the assessment of language and fine hand movements. The

Educational attainment = the maximum educational level that the patient completed in the Spanish educational system.

testing session was conducted a few days before surgery. The experimental testing was repeated between 4 and 6 weeks after surgery. We addressed the main and the secondary questions in this study—the effects of SMA damage in WM and verbal fluency—using patients' pre-surgery data. We used patients' postsurgery data only to conduct complementary analyses examining post-surgery changes in WM, verbal fluency, and PS (see Data analyses and Results sections for further details).

#### Tasks

#### WM Assessment—WM Index of the WAIS-III

The WM Index is derived from 3 tasks of the WAIS-III (Spanish Edition; Wechsler, 2001), which need to be administered in the same order as we describe them here: Digit span (Dspan), Letter-Number sequencing (LNS), and Arithmetic. The sum of the raw scores for each task gives the raw score for the WM Index.

#### **Dspan**

This task is composed of two parts: the Dspan forward and the Dspan backward. For the Dspan forward, the experimenter utters strings of digits at a rate of one number a second, approximately. The participant needs to repeat those digits in the same order the examiner uttered them. There are 8 blocks with 2 trials for each string length, which starts with 2 digits and increases by 1 in each successive block. The administration of the task is terminated if the participant fails both trials within the same block. The same procedure is used for the Dspan backward, with the exception that the participant needs to repeat the digits in reverse order and that only seven (instead of eight) blocks compose the task. The Dspan raw score is the sum of the total number of correct Dspan forward and Dspan backward trials.

#### **LNS**

The experimenter utters strings of items consisting of different numbers and letters presented in mixed order and at a rate of one item a second, approximately. The participant needs to

FIGURE 2 | Example of an SMA lesion (in these images, left is right and right is left). (A) A FLAIR sequence MRI shows a tumoral lesion involving the left SMA in a 35-year-old patient. (B) A FLAIR sequence MRI 6 months after surgery shows a complete resection, achieved by performing awake brain mapping surgery. The pathology report was anaplastic oligodendroglioma.

repeat each string, uttering first the numbers in numerical order followed by the letters in alphabetical order. There are 8 blocks with 3 trials for each string length, which starts with 3 items and increases by 1 in each successive block. The administration of the task is terminated if the participant fails all three trials within the same block. The LNS raw score is the sum of correct trials.

#### **Arithmetic**

The experimenter reads arithmetic problems out loud and asks the participant to give the correct answer to them. The participant is not allowed to take any written notes but the experimenter can repeat the entire problem once, if requested. Complexity in terms of the amount of information that needs to be held, the difficulty of the mathematical operations required (additions, percentages, etc.), and the time allotted for problem solving increases with every problem. There are 20 problems, and the administration of the task is terminated after the participant fails to solve 4 consecutive problems. The participant is given 1 point for each problem correctly solved, the total sum of which composes the Arithmetic raw score.

#### EF Assessment—Verbal Fluency Tasks

Verbal fluency tasks involve a limited amount of time (usually 1 min) for the participant to name as many words as possible according to a key, which is usually semantic or phonemic. We tested verbal fluency using both types of keys. On the semantic fluency, the participant had 1 min to generate as many words belonging to the semantic category "animals" as she could. On the phonemic fluency, the participant had the same amount of time to generate as many words as possible that began with the letter P, excluding proper names and repetitions of the same word with different endings. The raw score for each type of fluency (semantic, phonemic) is the total number of words generated within the allotted time limit.

#### PS Assessment—PS Index of the WAIS-III

The PS Index is derived from 2 tasks of the WAIS-III (Spanish Edition; Wechsler, 2001): Coding-Digit Symbol (CDsymbol), and Symbol Search (Ssearch). The sum of the raw scores for the 2 tasks gives the raw score for the PS Index.

#### **CDsymbol**

The participant is provided with a key matching each digit (from 1 to 7) with different meaningless symbols: e.g., digit 1 is associated with a horizontal double-headed arrow, digit 2 with a vertical double-headed arrow, digit 3 with 3 parallel horizontal lines, etc. Below this digit-symbol matching key—which remains always visible to the participant to avoid WM load effects—the participant is provided with a series of numbers, each placed above a blank box. She needs to draw the appropriate symbol in the box below each number according to the digit-association key. The participant is instructed to complete as many boxes as she can within the allotted time of 2 min. The CDsymbol raw score is the sum of the correctly completed boxes.

#### **Ssearch**

The participant is provided with 2 meaningless symbols in a left column. She needs to indicate if at least 1 of those 2 symbols is present in a group of 5 meaningless symbols in a right column. The participant gives the response by marking the "yes" or the "no" box. She is instructed to complete as many trials as possible in the allotted time of 2 min. The Ssearch raw score is the sum of correct answers minus the sum of errors.

# DATA ANALYSES

To reduce the skewness in the distribution of the data, we used transformed scores for data analyses. In the case of all WAIS-III tasks and verbal fluencies, we transformed raw scores into standard scores using Spanish normative data: WAIS-III Spanish edition (Wechsler, 2001) and NEURONORMA (Peña-Casanova et al., 2009), respectively. As for the WM and PS Indexes, we used intelligence quotient (IQ) scores: standard scores of the tasks composing each index were summed and transformed into IQs according to Spanish normative data (Wechsler, 2001). All data analyses were conducted with R (version 3.4.1; R Development Core Team, 2008).

Potential outliers were identified with box plots. In small samples it is difficult to determine whether an extreme observation is in fact outlying or merely reflects variability, especially in the case of patients. Therefore, we only considered as outlying the scores of control participants that fell below the cut-off for normality according to normative data. We based this action on the rationale that, by definition, controls cannot show impairment. This only occurred with two control participants: control c04 was not considered for any group analysis related to WM because he scored below the cut-off in most WM measures (**Supplementary Figures 1A–C**); control c06 was an outlier in phonemic fluency (**Supplementary Figure 1D**). In between-group analyses, we also excluded the data of the two patients matched socio-culturally with controls c04 and c06: p04's WM data, and p06's phonemic fluency data.

We first performed non-parametric between-group analyses (using the Mann Whitney U test) to examine whether the median (Mdn) of the patient group in the different tasks differed from that of the control group. Second, we assessed whether any potential impairment in WM observed in patients was actually driven by difficulties in PS. This assessment was done partialling out the influence of PS on WM measures in regression analyses and using the residuals resulting from those regressions to compare patients and controls. A similar procedure was used to assess whether between-group differences in verbal fluency were merely a by-product of patients' WM impairment rather than due to differences in EC. Finally, we included complementary analyses to examine whether the surgical treatment of the lesion caused any changes in WM, verbal fluency, or PS. To this end, we performed non-parametric within-group analyses (using the Wilcoxon signed rank test) to compare patients' pre- and post-surgery medians in the different cognitive measures.

# RESULTS

**Table 2** summarizes patients' and controls' scores in each WM, verbal fluency, and PS measure. It also summarizes the results of all between-group comparisons.

# Between-Group Differences in WM

The WM Index of patients (Mdn = 92) was significantly lower than that of controls (Mdn = 110; W = 92.5, p = 0.038), suggesting that SMA lesions led to WM impairment. To examine what WM task was more problematic for patients, we compared the performance of patients and controls in each WM task separately: patients clearly performed worse than controls on LNS (patients' Mdn = 9, controls' Mdn = 13, W = 105.5, p = 0.003); patients also showed poorer performance than controls in Arithmetic, being this between-group difference marginally significant (patients' Mdn = 8, controls' Mdn = 12, W = 89.5, p = 0.059); and there were no significant betweengroup differences in Dspan (patients' Mdn = 10, controls' Mdn = 12, W = 84.5, p =0.118). These results suggest that, in general, patients performed worse than controls in WM tasks, with difficulties appearing in the ex-WM tasks (LNS and Arithmetic) rather than in the att-WM one (Dspan) (**Figures 3A–D**).

# Between-Group Differences in Phonemic and Semantic Fluency

Patients performed poorly compared to controls in phonemic fluency (patients' Mdn = 7, controls' Mdn = 11, W = 95.5, p = 0.022), but not in semantic fluency (patients' Mdn = 7, controls' Mdn = 9, W = 98.5, p = 0.13). That is, patients showed reduced verbal fluency if tested with the phonemic fluency task (**Figures 3E,F**).

#### Between-Group Differences in PS

**Figure 3G** shows a marginally reduced PS Index for patients compared to controls (patients' Mdn = 100.5, controls'

Numbers in participants' code indicate the patient-control pairing (e.g., c01 is the control participant for p01). WM and PS Indexes are measured in terms of intelligence quotient. The values for the rest of the measures are standard scores. W/p-value (residuals): the significance of the between-group comparison partialling out PS (WM measures) or WM (phonemic fluency). \*Statistical values reported without considering these participants as they were considered outliers (or the patient pair of an outlier control participant).

Mdn = 107.5, W = 104.5, p = 0.062). Such reduction in PS was mainly driven by patients performing worse than controls in CDsymbol (**Figure 3H**; patients' Mdn = 8, controls' Mdn = 11.5, W = 105, p = 0.056) rather than in Ssearch (**Figure 3I**; patients' Mdn = 11, controls' Mdn = 12, W = 95, p = 0.186).

#### PS Influence on WM

Given the marginal impairment in PS shown by patients, we examined whether patients' difficulties with WM could be merely driven by slow PS. To this end, we first partialled out the contribution that PS had on participants' WM performance with linear regression analyses. We conducted a separate regression including both controls and patients—for each WM measure (dependent variables). We used CDsymbol performance as the predictor factor, which was the PS task in which patients and controls differed the most. Then, we compared the residuals of controls and patients resulting from those regressions: significant between-group differences in these residuals would mean that patients and controls differed in WM regardless of PS. The results of these analyses showed a reduced WM Index for patients compared to controls (W = 30.5, p = 0.05), which was mainly driven by a poorer performance in LNS (W = 21.5, p = 0.011) rather than Dspan (W = 46, p = 0.36) or Arithmetic (W = 39, p = 0.17). Arithmetic seemed to be the WM task most influenced by PS, as the marginal between-group difference reported above vanished after controlling for PS. In spite of that, after removing the influence of PS on WM, patients showed a similar pattern of WM difficulties as the one reported above: they still showed a reduced WM Index due to poor performance on an ex-WM task: LNS. This indicates that patients' difficulties in ex-WM were genuine and not reducible to slow PS.

# WM Influence on Phonemic Fluency

As exposed in the Introduction, besides EC processes, verbal fluency tasks also engage WM. This means that patients' reduced verbal fluency observed in the phonemic task version could have been driven by difficulties with its WM component rather than by EC deficits. To examine this possibility, we first partialled out the contribution of WM on participants' performance in phonemic fluency (dependent variable) with a linear regression analysis that included both controls and patients. We used LNS performance as the predictor factor in this regression, which was the WM task in which patients showed difficulties. Then, we compared controls and patients in the resulting residuals: significant between-group differences in these residuals would mean that patients and controls differed in the EC processes typically engaged by verbal fluency tasks regardless of their WM component. Contrasting with this prediction, however, the results showed no between-group differences in the residuals (W = 49.5, p = 0.97), indicating that patients' had no difficulties in engaging the EC processes required by the task. In other words, the reduced phonemic fluency reported above seemed to have been driven by patients' WM impairment.

# Complementary Analyses on Post-surgery Changes

Prior studies focused on movement and language seemed to indicate that the surgical treatment of the lesion reduces the likelihood that patients remain with permanent sequelae (Gabarrós et al., 2011). In this study, however, resecting the SMA lesion did not result in any changes with respect to WM or PS function, whereas patients' performance in verbal fluency tended to worsen: WM Index (pre-surgery Mdn = 90, postsurgery Mdn = 86, V = 21, p = 0.759), Dspan (pre-surgery Mdn = 10, post-surgery Mdn = 8, V = 16, p = 0.733), LNS (pre-surgery Mdn = 9, post-surgery Mdn = 8, V = 21, p = 0.268), Arithmetic (pre-surgery Mdn = 7, post-surgery Mdn = 9, V = 21, p = 0.67), PS Index (pre-surgery Mdn = 103, post-surgery Mdn = 92, V = 45, p = 0.306), CDsymbol (presurgery Mdn = 8, post-surgery Mdn = 9, V = 26, p = 0.719), Ssearch (pre-surgery Mdn = 11, post-surgery Mdn = 10, V = 43, p = 0.125), phonemic fluency (pre-surgery Mdn = 7, post-surgery Mdn = 4, V = 45.5, p = 0.072), and semantic fluency (presurgery Mdn = 7, post-surgery Mdn = 5, V = 38, p = 0.074). Many different factors may play a role in the post-surgery outcome of cognitive processes, including potential collateral effects of surgery, premorbid cognitive deficits, and various clinical variables such as extension and exact location of the lesion. Since we did not control for many of those factors, we will not discuss the post-surgery outcome any further.

# DISCUSSION

The main goal of the present study was to determine whether lesions affecting the SMA hamper WM by taking into consideration potential PS confounds. As a secondary goal we asked whether SMA damage would also hamper patients' performance on a broad, quick EC measure such as verbal fluency. We addressed these questions using tests commonly used in clinical practice to facilitate the transfer of the results to a health care context.

# On the Impact of SMA Lesions on WM

The results indicated that SMA lesions indeed compromised WM and that such a deficit could not be reduced to PS difficulties even though patients showed a trend toward slower PS compared to controls. The awake surgery data reported in Nakajima et al. (2014) had already evidenced a relation between SMA and WM. Neuroimaging data had also evidenced such relation. Indeed, different meta-analyses have attributed to the SMA a role in WM, regardless of the specific WM task, modality (verbal vs. non-verbal), and difficulty (Owen et al., 2005; Rottschy et al., 2012). However, neuroimaging data had also revealed that WM relies on a widespread fronto-parietal network, being the SMA one more of the various regions comprising such network along with portions of dorsolateral prefrontal, ventrolateral prefrontal, parietal, and subcortical regions (Owen et al., 2005; Rottschy et al., 2012). It is worth noting that such an extensive network could alleviate the effects of SMA damage to the extent that such damage would not have a significant impact on WM functionality. A neuropsychological approach was fundamental in shedding light onto this issue. However, WM deficits associated with SMA damage have never been previously reported, at least to our knowledge: as exposed in the Introduction, Nakajima et al.'s (2014) was the only study exploring the effects of SMA damage on WM, but the data obtained outside the operating room for the two single cases they reported were inconclusive. Therefore, the results of the present study represent the first evidence that the role of the SMA is relevant enough as to significantly impair WM in case of damage. In this regard, it is worth noting that, rather than the SMA playing a direct role in WM, it may play an indirect role through its connectivity with the rest of the brain regions composing the fronto-parietal network. This is a question for further research.

As for the specific pattern of WM difficulties, patients showed a reduced WM Index, which is a global WM measure. Among the different WM measures we had, WM deficits were captured by the LNS task. Patients did not differ from controls in Dspan, and Arithmetic, especially after removing the effects of PS. The fact that patients performed similarly to controls in Dspan indicated that they were relatively able to hold a series of items in WM. This means that their poor performance in manipulating such items (ex-WM)—which is what LNS mainly measures—could not be explained by patients' not having those items available. One would expect that this difficulty in manipulating information held in WM would, in turn, lead patients to perform poorly in tasks requiring operating with such information, as is the case with Arithmetic. It is relevant to note, however, that Arithmetic may not be the most valid tool to assess WM, as it depends on individuals' mathematical skills (Hill et al., 2010). In fact, in a study examining which tasks of the WAIS-III were the best predictors of WM capacity, Hill et al. (2010) found that Arithmetic was not among those predictors. These authors used common WM paradigms in experimental psychology as the gold standard in a study with 188 healthy individuals. The results of regression analyses indicated that, when considering only the 3 tasks originally thought to compose the WM Index (Dpan, LNS, and Arithmetic), Dpan and LNS accounted for most part of the variances (33% and 28%, respectively). In contrast, the Arithmetic was excluded from the model, evidencing its weak association with WM.

It is also relevant to point out that the present results should be taken only as a starting point for further research in which more sophisticated WM tools would be used. We acknowledge that the information one can gather with the WM Index of the WAIS-III regarding individuals' WM functioning is limited. Future research with experimental tasks is needed to fully understand the profile of WM difficulties associated to SMA damage. A related question for future research is whether such profile is specific to SMA patients with respect to the WM profile of patients with damage in other regions within the fronto-parietal network.

# On the Effects of SMA Lesions on Verbal Fluency

The secondary question of this study was to determine whether cognitive impairment (other than language) associated to SMA goes beyond WM and also affects EC due to its connectivity with the DLPFC (Nachev et al., 2008). To this end, we compared patients' and controls' performance in verbal fluency, both semantic and phonemic. In first between-group analyses, patients showed worse performance than controls in the phonemic task version. Subsequent analyses, however, revealed that the WM processes engaged by the phonemic fluency task drove such a disadvantage. That is, it seems that patients' reduced phonemic fluency was most likely not due to difficulties in the EC processes required by this task but to the WM ones. Note, however, that it would be too precipitate to state that these results suggest that SMA damage does not lead to EC deficits. This is because verbal fluency is too broad of an EC measure. In addition, it is not possible to disentangle the multiple EC processes this task may engage (e.g., inhibition, response suppression, and switching, among others; Abwender et al., 2001; Hirshorn and Thompson-Schill, 2006), which is a relevant limitation because patients may have selective difficulties in some of these processes. An additional limitation of verbal fluency is its strong dependence on the integrity of other cognitive processes different from EC, such as lexical access or semantic processing (Shao et al., 2014). Therefore, our data only indicates that SMA damage does not seem to affect cognitive processes engaged by verbal fluency other than WM. Future research using more extensive and specific experimental protocols is needed to further investigate the potential association of EC dysfunction and SMA damage.

A secondary point related to the verbal fluency task is the fact that patients did not differ from controls in the semantic task version (before partialling out the effects of WM). This observation suggests that semantic fluency engaged WM processes to a lesser extent than phonemic fluency and, thus, was not affected by the collateral effect of patients' WM impairment. In fact, in some authors' view, semantic fluency is particularly associated with lexical-semantic skills rather than WM or EC (Shao et al., 2014). In line with this view, neuropsychological studies have found that patients with brain damage that compromises regions typically associated with lexical-semantic representations show more difficulties with the semantic than with the phonemic verbal fluency task (Jones et al., 2006; Laws et al., 2010; Magaud et al., 2010; Meijer et al., 2011). Similarly, Satoer et al. (2014) described a patient with the SMA syndrome whose reduced semantic (but not phonemic) verbal fluency seemed to stem from his language difficulties rather than his WM or EC dysfunctions.

# On SMA Patients' Slow PS

As exposed above, patients showed a marginal PS impairment, which did not drive their WM impairment. Beyond this fact, it is worth discussing the potential origin of such marginal PS difficulties. In this respect, it is relevant to point out that there is no specific brain region subserving PS, but it depends on white-matter pathways that synchronize the transmission of information across distributed brain networks (Mesulam, 1998, 2000). This is why the neurological conditions most commonly affecting PS are those involving axonal damage, such as multiple sclerosis or traumatic brain injury (Rao, 1996; Levine et al., 2006). In a neuroimaging study using the CDsymbol task as a PS measure, Turken et al. (2008) revealed that, among the various white-matter pathways critical for PS, at least two of them involve the frontal cortex: the superior longitudinal fasciculus (a major fronto-parietal tract) and fronto-striatal projections. One of the frontal locations most relevant seemed to be the superior frontal cortex (SFC) to which parietal regions project through the superior longitudinal fasciculus. Another relevant frontal site was the DLPFC, connected with the basal ganglia through fronto-striatal projections. It is of note that the functioning of both frontal structures might be—at least to a certain extent compromised by SMA lesions: the SMA is, in fact, harbored in the superior and medial aspects of the SFC (Penfield and Welch, 1951) and linked to the DLPFC though white matter connectivity (Nachev et al., 2008). Therefore, the marginal PS impairment may have been due to the SFC and DLPFC (presumed) dysfunction having an impact on their parietal and striatal white matter links, respectively.

# Limitations of the Current Study

As acknowledged above, one of the limitations of the present study has to do with the simplicity of the tasks used to index WM and EC. In addition, the modest sample size has been a limitation in our statistical analyses. In this respect, it is worth underlining that the SMA's susceptibility to harbor tumors and other abnormalities calls for research on the SMA syndrome. However, the incidence of SMA damage is not massive, making it difficult to compose a patient group of a decent size: for example, the 12 patients in this study were recruited over a 3-year period in a hospital of reference for the treatment of SMA lesions in Spain.    In this regard, it is worth highlighting that this is the first group study investigating the effects of SMA lesions on cognitive functions other than language: the only prior study assessing non-linguistic cognitive functions only provided descriptive data of 2 single cases (Nakajima et al., 2014). In this respect, the results of the present study represent a remarkable new contribution to the research field. Even so, we interpret such results with caution.

It is also worth mentioning that a number of clinically related variables—which we did not control for—may have an influence on the extent of functional deficits. Two of these variables are the size and exact location of the lesion. In this respect, distinguishing between the 2 anatomical portions of the SMA—the pre-SMA, and the SMA-proper—would be of particular relevance. It seems that the former could be more related to cognitive processes (Chouinard and Paus, 2010). Therefore, it is possible that WM deficits arise only if the lesion affects the pre-SMA. In the case of a tumor, whether it invaded or displaced the SMA may also make a difference. In addition, it has been reported that the damage a tumor may cause is much greater if it is infiltrating (Gabarrós et al., 2011). Similarly, the degree to which (if any) the contralateral non-dominant SMA takes over the functions of the dominant one may vary across patients. It is worth noting, however, that a considerable large sample of patients would be needed to apply sound methodological approaches (e.g., neuroimaging studies based on lesion mapping techniques) and sound statistical methods (e.g., mixed-model regression analyses) in order to examine the implications of these variables.

#### CONCLUSION

The results of this study represent the first evidence that WM impairment is a symptom of the SMA syndrome, which seems to stem from difficulties in manipulating information held in WM. PS is also somewhat compromised in SMA patients. However, WM deficits are not reducible to PS difficulties. These findings highlight the need to include WM assessment in clinical SMA protocols. Further research is needed to establish the specific WM profile of SMA patients and determine the consequences of SMA damage for other cognitive functions such as EC.

#### ETHICS STATEMENT

This study was reviewed and approved by the ethics committee of the Hospital Universitari de Bellvitge (L'Hospitalet de Llobregat,

#### REFERENCES

Barcelona, Spain). The study was conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent.

#### AUTHOR CONTRIBUTIONS

All authors listed, have made substantial, direct and intellectual contribution to the work, and approved it for publication.

#### ACKNOWLEDGMENTS

MH was supported by the Ramón y Cajal (RYC-2016-19477) research program (Spanish Ministry of Economy, Industry, and Competitiveness). MH also acknowledges financial support from the Diputación Foral de Gipuzkoa/Gipuzkoako Foru Aldundia (Programa Fellows Gipuzkoa de atracción y retención de talento).

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg. 2018.00765/full#supplementary-material

Supplementary Figure 1 | Boxplots showing outlier control subjects in different WM and EC measures. (A–C) WM measures in which control c04 was an outlier. (D) EC measure in which control c06 was an outlier.

intelligence: a latent-variable approach. J. Exp. Psychol. Gen. 128, 309–331. doi: 10.1037/0096-34<IP_ADDRESS>9

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2018 Cañas, Juncadella, Lau, Gabarrós and Hernández. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Executive Functions Rating Scale and Neurobiochemical Profile in HIV-Positive Individuals

Vojislava Bugarski Ignjatovic1 \*, Jelena Mitrovic2 , Dusko Kozic3 , Jasmina Boban3 , Daniela Maric4 and Snezana Brkic4

1 Department for Psychology, Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia, 2 Department for Psychology, Faculty of Philosophy, University of Novi Sad, Novi Sad, Serbia, 3 Department for Radiology, Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia, 4 Department for Infectious Diseases, Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia

The set of complex cognitive processes, that are necessary for the cognitive control of behavior, known as executive functions (EF), are traditionally associated with the prefrontal cortex and commonly assessed with laboratory based tests and conventional neuroimaging. In an effort to produce a more complete and ecologically valid understanding of executive functioning, the rating scales have been developed in order to assess the behavioral aspects of EF within an everyday real-world context. The main objective of this study was to examine the relationship between behavioral aspects of EF measured by rating scale and neurometabolic profile in neurologically asymptomatic HIV-positive individuals under cART, measured using multi-voxel magnetic resonance spectroscopy (mvMRS). The sample comprised 39 HIV-positive adult male participants, stable on cART and 39 healthy HIV-negative volunteers. Both groups completed the Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A). HIV-positive group additionally underwent long-echo three-dimensional mvMRS to determine neurobiochemical profile in the anterior cingulate gyrus (ACG) of both hemispheres. Three dominant neurometabolites were detected: N-acetyl aspartate (NAA), the neuronal marker; choline (Cho), the marker of membrane metabolism and gliosis and creatine (Cr), the reference marker. Ratios of NAA/Cr and Cho/Cr were analyzed. The initially detected significant correlations between age, current CD4, BRIEF-A subscales Inhibit, Shift, Emotional Control, Plan/Organize, Self Monitoring and ratios of NAA/Cr and Cho/Cr in the dorsal and ventral part of the ACG, were lost after the introduction of Bonferroni corrections. Also, there were no significant differences between HIV–positive and HIV–negative group on any of BRIEF-A subscales. Such results possibly imply that stable cART regimen contributes to preservation of behavioral aspects of EF in asymptomatic HIV-positive individuals. Even though a subtle deficit in some aspects of EF might exist, it would not be manifest if behavioral aspect was assessed using EF rating scale. Further explanation might be that expected HIV-related changes in neurometabolic profile of the ACG under cART are not reflected in those behavioral aspects that are measurable by EF rating scale.

#### Edited by:

Antonino Vallesi, Università degli Studi di Padova, Italy

#### Reviewed by:

Anna Rita Egbert, University of British Columbia, Canada Keenan Alexander Walker, Johns Hopkins Medicine, United States

#### \*Correspondence:

Vojislava Bugarski Ignjatovic vojislava.bugarski-ignjatovic@ mf.uns.ac.rs

#### Specialty section:

This article was submitted to Cognition, a section of the journal Frontiers in Psychology

Received: 20 February 2018 Accepted: 27 June 2018 Published: 19 July 2018

#### Citation:

Bugarski Ignjatovic V, Mitrovic J, Kozic D, Boban J, Maric D and Brkic S (2018) Executive Functions Rating Scale and Neurobiochemical Profile in HIV-Positive Individuals. Front. Psychol. 9:1238. doi: 10.3389/fpsyg.2018.01238

Keywords: executive functions, anterior cingulate gyrus, behavior rating scale, HIV infection, MR spectroscopy

# INTRODUCTION

The executive functions (EF) are defined as the control or self-regulatory functions that organize and direct all cognitive activity, emotional response, and overt behavior (Miyake et al., 2000; Anderson, 2008; Duggan and Garcia-Barrera, 2015). The term "executive functions" represent an umbrella term for the interrelated functions that are responsible for purposeful, goaldirected, and problem-solving behavior in the everyday, "real world" environment (Goldstein et al., 2014). These functions are unique to our experience as human beings and critical for the successful and adaptive everyday functioning (Duggan, 2014). Given the central importance of the executive functions for the direction and control of the dynamic "real world" behavior, the reliance on traditional performance measurements can lead to a limited, incomplete assessment (Gioia and Isquith, 2004; Isquith et al., 2013). Resulting from an effort to produce a more complete and ecologically valid understanding of executive functioning, a number of rating scales have been developed in order to assess the behavioral aspects of executive function within an everyday real-world context; these scales can potentially serve as an ecological validity index for clinical or laboratory findings (Isquith et al., 2013; Duggan, 2014; Silver, 2014). In this sense, executive function rating scales were originally intended to serve as complementary measures to traditional assessment methods; however, studies have shown that performance-based and rating-based measurements assess different aspects of executive functioning and provide important complementary information to clinicians and researchers (Isquith et al., 2013; Toplak et al., 2013; Duggan, 2014; Silver, 2014).

According to Duggan (2014), some of the primary strengths of executive function rating scales are the ability to assess application of executive skills (rather than the functionality of their components), the capacity to capture executive characteristics of everyday functioning in clinical setting, the contributions of distinct information to executive function assessment, and the potential correlations with biological markers (Gioia et al., 2010; Isquith et al., 2013; Toplak et al., 2013). The presumed neurological basis for deficits in EF makes the relationship between markers of neuronal function and ratings on these scales an expected finding, especially in clinical populations (Isquith et al., 2013). Several studies reported associations between neural substrates and everyday executive functioning measured by rating scales (Isquith et al., 2013). The Behavior Rating Inventory of Executive Function (BRIEF; BRIEF-A) represents one of the most successful EF rating scales in daily activities, both in pediatric and adult clinical population.

Frontal lobes are traditionally said to be involved in the executive functions. However, due to the high diversity of cognitive processes encompassed by the term "executive functions," researchers put an effort to define functions within the executive domain according to the cognitive process that they are involved in. Functional neuroimaging studies have shed much light on this field, by identifying the cerebral regions engaged in the processing of certain cognitive task (Gazzaniga, 2004; MacPherson et al., 2015). However, these conclusions were not sufficient. The results of lesion studies also pointed to the frontal lobe regions as necessary for performing specific cognitive tasks (MacPherson et al., 2015).

The anterior cingulate gyrus (ACG) is a unique and important region in the brain circuitry, extensively connected to both the emotional and the cognitive regions. ACG lies in the medial part of both cerebral hemispheres, encompassing the corpus callosum. It is divided into two anatomically and functionally different regions, the ventral (or rostral) ACG (surrounding the genu of the corpus callosum) and the dorsal (or caudal) ACG (surrounding the body of the corpus callosum) (Palomero-Gallagher et al., 2009). Anatomic specificum of these regions (the whole dorsal and a half of ventral part) is the presence of Von Economo spindle neurons that are larger than pyramidal neurons, constructed for purposes of fast neurotransmission and high connectivity (Nimchinsky et al., 1999). The connections of these two parts differ greatly, as well as their proposed functions. The ventral part of ACG has connections with emotion (amygdala), autonomic (hypothalamus), memory (hippocampus) and reward-related (orbitofrontal cortex) regions of the brain. On the other hand, the dorsal part of ACG has rich connections with cognitive (lateral prefrontal) and motor-related (premotor and primary motor) regions, as well as with thalamic nuclei involved in pain- and motor-processing (Allman et al., 2001).

Functional roles of these two parts of ACG have been studied using functional neuroimaging studies and diffusion tensor imaging (DTI). DTI studies, that measured structural connectivity, largely confirmed patterns described in anatomical analyses (Stevens et al., 2011). Resting-state functional MR imaging (fMRI) revealed the connections between the ventral ACG and areas involved in affective processing, as well as between the dorsal ACG and sensorimotor and cognitive processing (Margulies et al., 2007). Task-related fMRI showed that motor-related tasks activated dorsal ACG, while simple emotions (sadness and happiness) activated more the ventral ACG. However, the sense of sadness contributed to the sense of pain, processed by the dorsal ACG. Cognitive control, conflictmonitoring, response-selection, error-detection and emotionrelated appraisal are most likely processed also by the "cognitive" dorsal part of ACG. The ACG is thus the "affective" part, involved in processing emotion assessment, emotion-related learning and autonomic regulation (Etkin et al., 2011). Prior neurocognitive and neuroimaging studies in healthy population confirmed the association between anterior cingulate gyrus (ACG) and cognitive control functions, especially in conflict detection, performance monitoring and response selection (Botvinick, 2007; Alexander and Brown, 2017), as well as consolidation of memories (Nieuwenhuis and Takashima, 2011) and attention and reward-based learning (Hayden et al., 2011). Also, a functional division of ACG in ventral "affective" and dorsal "cognitive" subdivisions was confirmed (Mohanty et al., 2007).

Human Immunodeficiency Virus (HIV) infection has become a chronic condition after the introduction of combination antiretroviral therapy (cART), with the average lifespan of HIV-positive individuals reaching that of the HIV-negative population (Heaton et al., 2015). Currently, European AIDS Clinical Society guidelines strongly recommend the introduction of cART immediately after confirmation of HIV-seropositive status, regardless the immune status (Ryom et al., 2016). This early introduction of cART has significantly changed the overall picture of HIV-associated neurocognitive disorders, with a marked reduction in the prevalence of the most severe form, HIV-associated dementia (Chan and Brew, 2014) being up to 18% in the pre-cART era, and dropping down to 3–5% after the introduction of modern antiretroviral treatment (Heaton et al., 2011) The milder forms, asymptomatic neurocognitive impairment, and mild neurocognitive impairment remain highly prevalent in the cART era (Harezlak et al., 2011; Clifford and Ances, 2013). HIV-positive individuals have become an interesting clinical population for the assessment of cognitive status. Especially interesting are the EF, since they play a very important role in adherence to the antiretroviral therapy (Huerta et al., 2016). Recent studies showed impairments in multiple domains of executive functions in HIV-infected individuals (Heaton et al., 2011; Cattie et al., 2012; Giesbrecht et al., 2014), such as performance difficulties observed on cognitive shifting and complex sequencing tests (Marcotte et al., 1999; Mindt et al., 2003; Vazquez-Justo et al., 2003; Heaton et al., 2004;); response inhibition (Hinkin et al., 1999; Yadavalli, 2009); decision making (Hardy et al., 2006); abstract reasoning (Marcotte et al., 1999; Heaton et al., 2004); planning (Cattie et al., 2012) and working memory (Malagurski et al., 2017). In spite of peripherally successful cART application and the absence of a subjective experience, a certain degree of the cognitive deficit can still be observed (Malagurski et al., 2017). In addition, in the cART era, an important contribution to neuropsychiatric complications can be attributed to the antiretroviral drugs–nucleoside and non-nucleoside reverse transcriptase inhibitors in the first line (Treisman and Soudry, 2016).

One of the most commonly used advanced neuroimaging techniques for the detection of pathological process underlying neurocognitive impairment in HIV-positive population is magnetic resonance spectroscopy (MRS). MRS is a noninvasive diagnostic imaging technique used for detection of neurometabolite concentrations in small volumes of brain tissue. The pattern of changes in neurometabolic profile can characterize the type of ongoing pathological process in the brain parenchyma (Posse et al., 2013). Classical pattern of brain injury in chronic HIV infection consists of the decline in neuronal marker (N-acetyl-aspartate, NAA), and the increase in markers of membrane metabolism (choline-containing compounds, Cho) and glial proliferation (myoinositol, mI) (Ances and Hammoud, 2014). Recent MRS studies showed a reduction in neuronal marker in ACG in the HIV-positive subjects compared to healthy controls (Boban et al., 2017). Additionally, some differences were observed in the level of NAA in HIV-positive subjects with stimulant (alcohol, drugs) dependence, compared to both healthy controls and HIV-positive subjects without this dependence (Taylor et al., 2000). However, correlations between neurometabolite ratios and executive functions in chronic HIV infection have not been widely studied to date.

Although there are many recent studies that examined the use of EF traditional performance measurements in HIV-positive adult population, to date there are no studies that used EF rating scales. In addition, there are no studies that explored the relationship between neurometabolite ratios measured using advanced neuroimaging techniques such as multi-voxel MRS and behavior measured by EF rating scales, in HIV-positive adult population.

The first objective of this study was to assess the correlations between EF rating scale subtests and neurometabolite ratios obtained on the three-dimensional MRS in the ventral and dorsal ACG in chronically infected, virally suppressed HIV-positive individuals that are stable on long-standing cART.

The main hypothesis resulting from this objective was that there was a significant correlation between NAA/Cr, Cho/Cr ratios and EF rating scale subtests–better achievement on Working Memory, Emotional Control, Inhibit and Shift subtests would be followed by higher NAA/Cr and lower Cho/Cr ratios, especially in the "cognitive" dorsal part of the ACG.

Previous studies on neurocognitive status in HIV-positive individuals showed the presence of asymptomatic neurocognitive impairment even in subjects who are under stable cART regimen, thus supporting the assumption that HIV undoubtedly had certain negative influence on the central nervous system.

In well-controlled HIV infection, cognitive impairment is often subtle, discrete and commonly asymptomatic. Based on those findings, it can be assumed that the probability of detecting the cognitive deficit among people on a stable cART regimen on behavioral tests is relatively low, but possible.

The second objective of this study was to find out whether there was a difference between the group of HIV-positive individuals on stable cART and the group of HIV-negative healthy controls, on the EF rating scale subtests. It was hypothesized that there was a difference only on certain subtests, such as Working memory, Shift, Inhibit and Emotional control.

# MATERIALS AND METHODS

# Participants

This institutional ethical board-approved cross sectional study was conducted from 2015 to 2016 and comprised a total of 39 HIV-positive adult male participants, average age 42.18 years (range 25–66). All participants were Caucasians. All participants were chronically infected (over 1 year after diagnosed) and stable on cART (over 1 year on the same cART regimen). HIV-positive subjects were diagnosed with HIV infection using polymerase chain reaction (PCR) testing. After the initiation of cART, viral loads and current CD4 T-cell counts were closely monitored in each patient. Plasma viral load was under the detection threshold and current CD4 counts were over 250 cells/mm3 in all participants for at least 1 year. In addition, the regimen of cART remained unchanged for the same period of time. Based on the results of the screening neurocognitive test, International HIV Dementia Scale, (Sacktor et al., 2005), all participants were neurologically asymptomatic and able to work.

The criteria for inclusion of HIV-positive individuals in the study were: (1) age over 18, (2) normal conventional MR scan,

TABLE 1 | Clinical and sociodemographic characteristics for the study sample.

(3) the presence of HIV infection (PCR verified), (4) HIVseronegative status on PCR testing during previous 12 months (two negative PCR tests on a regular 6-months follow up), and (5) plasma viral load under the level of detection (<40 copies/mL). The exclusion criteria were the presence of focal or diffuse lesions in the brain (verified by conventional MR scan), active infiltrative or infective/opportunistic neurological disease, chronic neurological or psychiatric illness, comorbid disorders known to influence cognitive performance such as diabetes, cardiovascular diseases, hepatitis B or C, active abuse of alcohol and narcotic drugs. The route of infection was registered and in all HIV-positive participants and it was via sexual transmission. No illicit intravenous drug use or vertical transmission was detected in our cohort.

The control group consisted of 39 adult male participants, average age 42.21 years (range 25–65), Caucasian race, who were more than 18 years old and tested HIV-negative. The selection of the sample was performed randomly from general population. Healthy controls underwent neuropsychological testing with BRIEF-A. However, in these participants, neuroimaging was not performed due to the lack of clinical indication.

All subjects signed the fully informed written consent to participate in this study. Therefore, every participant was familiar with the research objectives. Demographic and clinical data of the participants in the study are summarized in **Table 1** and descriptive statistics for the variables included in the study are summarized in **Table 2**. The difference between HIV+ and HIV– participants was not significant regarding educational achievement [t(76) = −1.968, p > 0.05] as well as age [t(76) = −0.010, p > 0.05].

#### Measures

Behavior Rating Inventory of Executive Function-Adult Version–BRIEF-A (Roth et al., 2005) is the standardized rating scale developed to assess behavior manifestations of executive functions in adults, aged 18–90 years. The BRIEF-A consists of Self-Report and Informant Report Forms. The Self-Report Form, used in this study, provides an understanding of the individual's perspective with respect to their own difficulties in self-regulation. The questionnaire contains 75 items within nine non-overlapping clinical scales: Inhibit, Shift, Emotional TABLE 2 | Descriptive statistics for the variables included in the study.

control, Initiate, Working Memory, Plan/Organize, Organization of Materials, Task Monitor and Self-Monitor. Responses were indicated on a 3-point scale labeled never, sometimes, often, where lower score represents better performance. Reliability analyses, measured by Chronbach's Alpha, revealed high internal consistency of all clinical scales for this study sample (Inhibit α = 0.808, Shift α = 0.701, Emotional Control α = 0.878, Self Monitor α = 0.811, Initiate α = 0.725, Working Memory α = 0.778, Plan/Organize α = 0.670, Task Monitor α = 0.651, Organization of Materials α = 0.813).

 Conventional MR scan consisted of multisequentional and multiplanar tomograms, necessary for excluding focal or diffuse white and gray matter lesions, and for localization of multivoxel network. Conventional MR imaging consisted of: T1-weighted sagittal, T2-weighted and FLuid Attenuation Inversion Recovery (FLAIR) axial and T2-weighted coronal tomograms, as well as thin-sliced 3D T1-weighted magnetization prepared rapid acquisition gradient echo (MPRAGE) sagittal tomograms. Threedimensional multi-voxel magnetic resonance spectroscopy was

spectra were analyzed.

performed using point-resolved spectroscopy with a long echo time (time of repetition 1,700 ms/time of echo 135 ms). The volume of interest (VOI) was 80 × 80 × 80 mm, with a slice thickness of 10 mm, positioned parallel to the axial images. Total scan time was 7:17 min (weighted phase-encoding scheme was applied). Saturation planes (for saturating signals of surrounding tissues) were manually positioned along the margin of the VOI. The automatic, volume selective shimming method was used to optimize homogeneity of the magnetic field. VOI was positioned in the same way in every subject in order to achieve the reproducibility, while analyzed voxel positions were chosen manually. Multivoxel network was placed in the supracallosal white matter and gray matter, comprising gray matter of anterior and posterior cingulate gyrus and white matter of frontal centrum semiovale (**Figure 1**). Due to the reasons stated in Introduction, four localizations were chosen out of possible 64 voxels (8 × 8 network): two placed in the ventral part of anterior cingulate gyrus (one in the left and one in the right hemisphere) and two placed in the dorsal part of anterior cingulate gyrus (also one in the left and one in the right hemisphere). The spectra were imported to a digital workstation, where a dedicated manufacturer's software for spectroscopy was applied for baseline corrections, peak identification and calculation of the ratios throughout analyzed voxels. Spectra obtained on short echo time MRS were analyzed by identifying peaks of N-acetyl aspartate (NAA) at 2.02 ppm, choline (Cho) at 3.2 ppm, and creatine (Cr) at 3.0 ppm. Ratios of NAA/Cr and Cho/Cr were calculated for each voxel. Spectra from left and right side were analyzed separately, due to possible lateralization in some executive functions (emotional control and working memory, for example).

#### Analysis

#### Variables

Variables in this research were obtained as scores from nine BRIEF-A scales (constructed by adding the items belonging to a certain scale in a sum score) as well as ratios of NAA/Cr and Cho/Cr in dorsal and ventral part of the anterior cingulate gyrus (ACG) on both sides, in order to determine the relationship between executive functions and neurobiochemical profile. Due to the presumed lateralization of some executive functions (although chronic HIV-related changes affect the brain volume diffusely), we analyzed each voxel separately. Age, educational achievement, duration of HIV infection, duration of cART

\*\*p < 0.01, \*p < 0.05.

therapy, nadir CD4 count (the lowest CD4 count in patient's history), and current CD4 counts were examined in relationship with executive functions. Also, these parameters were examined as potential control variables in a regression model.

#### Data Analyses

All statistical analyses were performed using IBM SPSS software (Version 23.0, Chicago, IL, USA). Pearson's correlation analyses were conducted to estimate the relationship between neurobiochemical profile in normal-appearing brain tissue and executive functions, clinical parameters, sociodemographic variables and executive functions as well as the relationship between clinical parameters, sociodemographic variables, and neurobiochemical profile. Four Pearson's correlation analyses were conducted separately regarding specific areas of interest. In order to investigate the difference on nine BRIEF-A scales between HIV+ and HIV– individuals, nine t-tests were conducted. Moreover, five hierarchical regression analyses were conducted to estimate the unique contributions of neurobiochemical changes in normal-appearing brain tissue, in the prediction of executive functions.

Due to the small study sample and many hypothesis tested, the Bonferroni correction was introduced in order to control family wise error rate (FWE) and avoid making Type I error (Armstrong, 2014). The Bonferroni correction was used for Pearson's correlation tests since multiple comparisons were tested in a way that the alpha value (p-value) was adjusted by the number of comparisons being performed. To perform the Bonferroni correction, the p-value (p = 0.05) was divided by the number of comparisons for each test separately. For the first Pearson's analysis where the goal was to determine the relationship between clinical parameters, sociodemographic variables, and executive functions in HIV+ individuals, the p-value was calculated and the significance value was set at p = 0.0009. For the second Pearson's correlation analysis where the goal was to investigate the relationship between EF and neurobiochemical profile in HIV-positive individuals, the significance value was set at p = 0.003. In order to investigate the relationship between clinical parameters, sociodemographic variables, and neurobiochemical profile in HIV-positive individuals, statistical significance was set at p = 0.001. Finally, in order to determine the relationship between executive functions and sociodemographic variables in HIVindividuals, the significance value was set at p = 0.003.

However, since this correction was too rigorous and all statistically significant results were lost, we presented and discussed the obtained results as if the Bonferroni correction was not applied.

#### RESULTS

In order to determine the relationship between sociodemographic variables, clinical parameters and executive functions, Pearson's correlation test was performed (**Table 3**). Statistically significant positive correlations were obtained between the variable Age and BRIEF-A scale Shift as well as between current CD4 count and BRIEF-A scale Working Memory.

Pearson's correlation analysis was performed in order to determine the relationship between sociodemographic variables and EF (**Table 4**). Significant positive correlation was obtained between the educational achievement and Working Memory.

Pearson's correlation analysis was performed in order to determine the relationship between executive functions and neurobiochemical profile (**Table 5**). Statistically significant negative correlations were obtained between NAA/Cr at the dorsal part of the ACG (right) and Inhibit, Shift, Emotional Control, Plan/Organize as well as between Cho/Cr at the dorsal part of the ACG (right) and Self Monitoring. Furthermore, statistically significant negative correlations were obtained between NAA/Cr at the dorsal part of the ACG (left) and Shift, Emotional Control, and Self Monitoring.

In order to determine the relationship between sociodemographic variables, clinical parameters and neurobiochemical profile in HIV-positive individuals, Pearson's correlation test was performed. The results are summarized in **Table 6**: statistically significant correlations were obtained between NAA/Cr at the ventral part of the ACG (left) and nadir CD4 count, NAA/Cr at the dorsal part of the ACG (right)and age, as well as between the duration of cART, NAA/Cr at the dorsal ACG (left) and age.

Nine t-tests were conducted in order to investigate the difference between HIV-positive and HIV-negative individuals on nine BRIEF-A scales. Observing the results (**Table 7**), we could state that there is no difference between age-matched HIV-positive and HIV-negative individuals with regard to EF.

Hierarchical regression analyses were performed in order to determine the influence of the NAA/Cr and Cho/Cr ratios at the dorsal ACG (left and right) on following BRIEF-A scales: Inhibit, Shift, Emotional Control, Self-monitoring, and Plan/Organize. Age was used as the control variable in the first step, the current CD4 count in the second step, and the NAA/Cr and Cho/Cr

TABLE 4 | Correlations between sociodemographic variables and BRIEF-A measures in HIV-negative individuals.

\*p < 0.05.

ratios at the dorsal ACG (left and right) in the third step. The results (Tables 8–17 in Supplementary Materials) showed that age had a significant influence on clinical scale Shift. However, none of the metabolite ratios had significant influence on the achievement BRIEF-A scales.

## DISCUSSION

Recent studies showed that HIV-positive individuals have a significantly extended lifespan due to the introduction of cART (Heaton et al., 2015). However, this resulted in a more frequent occurrence of asymptomatic neurocognitive impairment represented with subtle deficit in multiple domains of EF (Heaton et al., 2011; Cattie et al., 2012; Giesbrecht et al., 2014). Majority of studies that reported deficits in HIV-positive subjects, assessed the EF using limited, laboratory-based tests, known as traditional performance measures. Over time, however, clinicians and researchers revealed that these tests could not always detect impairment in patients who had clear executive dysfunction in their everyday lives, while, at the same time, in patients with no evidence of executive problems outside of the test setting, tests indicated impairment (Pennington and Ozonoff, 1996). Due to aforementioned reasons, the standardized rating scales of real world behavior are becoming increasingly used as a supplement to the traditional laboratory tests (Silver, 2014).

To date, to the best of our knowledge, there are no HIVrelated studies which used rating scales in the assessment of EF. This was the main reason for the authors of this study to use the behavior rating scale of EF in chronically infected, virally suppressed HIV-positive subjects under long-standing cART and in correspondent group of HIV-negative individuals, in order to detect potential differences on nine subscales of BRIEF-A

TABLE 5 | Correlations between neurobiochemical profile and executive functions in HIV-positive individuals.

VP ACG, ventral part of the anterior cingulate gyrus; DP ACG, dorsal part of the anterior cingulate gyrus. \*\*\*p < 0.001, \*\*p < 0.01, \*p < 0.05.

between these two groups. The results showed that there were no significant differences between HIV-positive and HIVnegative individuals on the subscales Inhibit, Shift, Emotional control, Initiate, Working Memory, Plan/Organize, Organization of Materials, Task Monitor and Self-Monitor. This was a rather surprising result, since at least subtle differences were expected primarily on Working memory, Shift, Inhibit and Emotional control subscales, that represented basic/core cognitive processes of EF and played an important role in organizing behavior. Also, these specific aspects of EF have been most thoroughly studied in clinical HIV-positive populations.

Some subtle, though asymptomatic neurocognitive deficits can be observed in HIV-positive individuals under cART using traditional performance measure tests (Malagurski et al., 2017). However, in our study, no significant deficit was recorded in any of nine scales of EF. Potential explanation could be that, even if a certain degree of disturbance existed in tested components of EF, this deficit was not pronounced enough to be observed in the behavioral domain. This could imply that in HIV-positive individuals under stable cART, who are functional in everyday activities and capable to work, there are no prominent deficits in the behavioral domain, even though they can be detected on traditional performance measure tests. Furthermore, it might be true that a behavioral deficit has to be more prominent to cause the disturbances in everyday, real life situations in HIV-positive patients. Indirect confirmation for this conclusion was that our sample included HIV-positive who were highly functional in everyday activities and that the level of compliance was high (reflected in plasma viral load under the detection limit). Additional explanation could lie in potential functional neuronal reorganization and brain plasticity that prevents further deprivation of executive functions. A recent study by Sanford et al. recently raised the issue whether the functional remodeling in the pathways involved in cognition existed in HIV-positive individuals under cART. In a recently published 2-year follow-up study, a better interval performance was observed on several neurocognitive tasks, explained as the result of a beneficial effect of the on-time introduction of cART and long-standing stable aviremia (Sanford et al., 2018).

The second main objective of the study was to examine the presence of correlations between EF rating scale subtests and neurometabolite ratios in the dorsal and ventral part of the ACG, obtained using multi-voxel MRS in chronically infected, virally suppressed HIV-positive individuals under stable and long-standing cART. The idea to explore these correlations came from everyday clinical practice, since we felt that obtained results could contribute to the better clinical management of HIV-positive individuals. Additionally, if significant correlations between neurometabolite ratios and executive functions were observed, it would be the additional confirmation of the key neuroanatomic role of ACG in EF, especially given that these correlations have never been examined in HIVpositive subjects. Also, the data obtained on this easy and fast assessment scale, suggesting the presence of neurodegeneration in the domains related with everyday activities, could lead the clinical decision on introducing further neuroimaging and detailed neurocognitive studies. That way, new protocols for the assessment of neurocognitive status in an individual patient could be established, based on the speed, efficacy and costbenefit analysis, thus contributing to the better initial triage of the patients eligible for the cognitive assessment.

We decided to examine correlations between BRIEF-A subscales and neurometabolite ratios in the ventral and dorsal parts of the ACG using Pearson's correlation analysis. Statistically significant correlations were obtained between NAA/Cr at the dorsal part of the ACG (right) and Inhibit, Shift, Emotional Control, Plan/Organize, as well as between Cho/Cr at the dorsal part of the ACG (right) and Self Monitoring. Furthermore, statistically significant correlations were obtained between NAA/Cr at the dorsal part of the ACG (left) and Shift, Emotional Control, and Self-Monitoring.

With regard to the obtained correlations, one can see that the majority of significant correlations involved scales of Behavioral regulation domain: Inhibit, Shift, Emotional Control and Self-Monitoring. Achievement on all these scales reflects the ability to maintain appropriate regulatory control of one's own behavior and emotional responses. This includes appropriate inhibition of thoughts and actions, flexibility in shifting problemsolving set, modulation of emotional response, and monitoring one's actions. Appropriate behavioral regulation is likely to be a precursor of appropriate meta-cognitive problem solving that successfully guides active and systematic problem solving, and more generally supports appropriate self-regulation (Isquith et al., 2013). Lower concentrations of neuronal marker (NAA/Cr) in the dorsal ACG (left and right) are connected to the poorer achievement on these scales. This result clearly confirmed that the dorsal ACG is the key structure in behavioral regulation, problem solving and monitoring of thoughts and actions. The decrease of NAA/Cr in the left and right dorsal ACG is related to the deprivation of inhibitory control and impulsivity, leading to the decline of the ability to resist impulses and the ability to stop one's own behavior at the appropriate time. The ability to move with ease from one situation, activity, or aspect of a problem to another as the circumstances demand is also deprived. Key aspects of shifting include the ability to make transitions, tolerate change, problem-solve flexibly, switch or alternate attention and change focus from one mindset or topic to another, implying that the progressive decline of all these shifting aspects occurs (Isquith et al., 2013). The decrease of NAA/Cr ratios in the dorsal ACG (left and right) was correlated with poor achievement on Emotional Control subscale, primarily with regard to the impact of EF problems on emotional expression and assesses an individual's ability to modulate or control his or her emotional responses. It is interesting that Self-Monitor scale was the only scale that showed correlations with Cho/Cr ratios in the right ACG. The decrease in both Cho/Cr and NAA/Cr ratios in the dorsal ACG (left and right) is correlated with impairment in the aspects of social or interpersonal awareness, since this scale captures the degree to which an individual perceives himself as aware of the effect that his/her behavior has on others (Isquith et al., 2013). From other scales involved in Metacognition domain, only the Plan/Organize scale showed positive correlation with NAA/Cr in the dorsal ACG on the

VP ACG, ventral part of the anterior cingulate gyrus; DP ACG, dorsal part of the anterior cingulate gyrus. \*\*\*p < 0.001, and \*p < 0.05.

MD, mean difference; SED, std. error difference.

right. The Plan/Organize scale measures an individual's ability to manage current and future-oriented task demands. This scale consists of two components: plan and organize. The Plan component captures the ability to anticipate future events, to set goals, and to develop appropriate sequential steps ahead of time in order to carry out a task or activity. The Organize component refers to the ability to bring order to information and to appreciate main ideas or key concepts when learning or communicating information (Isquith et al., 2013). Obtained correlations imply the key role of the dorsal ACG in the relationship between behavioral changes in everyday setting with the decrease in neuronal markers in chronic HIV-positive subjects.

The authors wanted to satisfy more strict statistical criteria and, therefore, the Bonferroni corrections were performed. However, after the introduction of the Bonferroni corrections, none of the initially detected correlations remained significant. The authors expected that correlations between those variables should exist, based on prior studies, both using multi-voxel MRS and other advanced neuroimaging techniques. Therefore, we decided to discuss the potential explanations for the absence of significant correlations after the introduction of this strict statistical criterion.

Given that a decrease in some neurometabolite concentrations in chronically infected virally suppressed HIV-positive subjects was observed in a prior study (Boban et al., 2017), we expected that this decrease could be correlated with disturbances in some behavioral aspects that are coordinated with EF. The lack of expected correlations after the introduction of Bonferroni corrections can be explained with several arguments. The first argument was that our sample was consisted of neuroasymptomatic HIV-positive subjects, under stable cART regimen, functional in everyday activities and able to work. We speculated that, although the presence of HIV can affect the neurometabolite ratios, the good clinical control of the disease prevented the overt profile in behavioral aspects (that could be correlated with expected deficits in executive functions). The lack of differences between HIV-positive and control subjects on BRIEF-A subscales indirectly confirmed that behavioral aspects of executive functions in asymptomatic and working HIV-positive individuals are not inferior to those in healthy controls. An additional explanation could follow the direction of potential neuronal remodeling and brain plasticity that prevented manifest deprivation of executive functions. The other argument deals with the issue of successful use of selfassessment scales (such as BRIFEF-A) in behavioral analyses of HIV-positive subjects, since the participants could have given socially appealing answers in order to present themselves as functional. This might justify the need for additional objective/ independent assessment obtained from family members or partners in future studies. The third argument might be that this type of scale was not sensitive enough to detect subtle deficits in behavioral functions in HIV-positive subjects, thus explaining the lack of correlations between rating scale and neurometabolite ratios.

When discussing the additional correlations between clinical parameters of HIV infection, sociodemographic variables and BRIEF-A subscales, in HIV-positive group, the significant correlations were observed between Shift scale and age. There were no significant correlations between age and behavioral aspects of EF in HIV-negative group, while there was a significant correlation between age and results on the Shift scale. (The more advanced the age, the worse achievement was detected on this scale). The explanation for the lack of correlations in the HIV-negative group could be the age of the sample (42.18), implying that correlations may be related with advanced age. The pattern of brain aging in HIV-positive population has also been changed with the early introduction of cART into the accelerated aging form (Cole et al., 2017), meaning that HIV infection increases the burden of risk for age-related comorbidities. The presence of the correlation between age and Shift scale, and the absence of any correlations with age in age-matched HIV-negative controls, might be in favor of this assumption.

Key aspects of shifting include the ability to make transitions, tolerate change, solve a problem flexibly, switch or alternate attention, and change focus from one mindset or topic to another. According to the relationship between age and Shift scale, this scale might particularly reflect those aspects of EF that are most vulnerable to the aging effect. This might be important in the context of compliance, working ability and global cognitive efficacy prediction in HIV-positive individuals.