4.Digit Span Reverse

Similar to "Digit Span-Forward" above, the patient is asked to repeat the sequence in reverse order. For example, suppose the patient is given the sequence 5–7 − 3, and the patient is asked to repeat it in the reverse order, "3–7-5". Exercises start with a 3-sequence digit and progress to an 8-sequence digit.


**3. Pseudoplastic infinite-acting radial flow regime in homogeneous formations**

Interpretation of pressure tests for non-Newtonian fluids is performed differently to conventional Newtonian fluids. During radial flow regime, Non-Newtonian fluids exhibit a pressure derivative curve which is not horizontal but rather inclined. As shown by [12], the smaller the value of *n* (flow behavior index) the more inclined is the infinite-acting pressure derivative line, see Figure 2.

A partial differential equation for radial flow of non-Newtonian fluids that follow a powerlaw relationship through porous media was proposed [11]. Coupling the non-Newtonian Darcy's law with the continuity equation, they derived a rigorous partial differential equation:

$$\frac{\stackrel{\frown}{\circlearrowleft}^{2}P}{\stackrel{\frown}{\circlearrowleft r}^{2}} + \frac{n}{r}\frac{\stackrel{\frown}{\circlearrowleft P}}{\stackrel{\frown}{\circlearrowleft r}} = c\_t \phi \text{n} \left(\frac{\mu\_{eff}}{k}\right)^{1/n} \left(-\frac{\stackrel{\frown}{\circlearrowleft P}}{\stackrel{\frown}{\circlearrowleft P}}\right)^{(n-1)/n} \frac{\stackrel{\frown}{\circlearrowleft P}}{\stackrel{\frown}{\circlearrowleft P}}\tag{1}$$

This equation is nonlinear. For analytical solutions, a linearized approximation was also derived by [11]:

$$\frac{1}{r^n} \frac{\partial}{\partial r} \left( r^n \frac{\partial P}{\partial r} \right) = Gr^{1-n} \frac{\partial P}{\partial t} \tag{2}$$

Where:

$$\mathbf{G} = \frac{\mathbf{3792.188n\phi c\_t\mu\_{eff}}}{k} \left( \mathbf{96681.605\frac{h}{qB}} \right)^{1-n} \tag{3}$$

and,

$$
\mu\_{eff} = \left(\frac{H}{12}\right) \left(9 + \frac{3}{n}\right)^n \left(1.59344 \times 10^{-12} \, k\phi\right)^{(1-n)/2} \tag{4}
$$

**Figure 2.** Pressure derivative for a pseudoplastic non-Newtonian fluid in an infinite reservoir – After Reference [12]

The dimensionless quantities were also introduced by [10] as

156 New Technologies in the Oil and Gas Industry

and successfully tested with synthetic data.

**formations**

equation:

derived by [11]:

Where:

and,

derivative line, see Figure 2.

As far as non-Newtonian fluid flow through naturally fractured reservoirs is concerned only a study presented by [19] is reported in the literature. He presented the analytical solution for the transient behavior of double-porosity infinite formations which bear a non-Newtonian pseudoplastic fluid and his analytical solution also considers wellbore storage effects and skin factor; therefore, [2] used the analytical solution without wellbore storage and skin introduced by [19] was used to develop an interpretation technique using the pressure and pressure derivative, so expressions to estimate the Warren and Root parameters [26] (dimensionless *storage coefficient and interporosity flow parameter)* were found

**3. Pseudoplastic infinite-acting radial flow regime in homogeneous**

Interpretation of pressure tests for non-Newtonian fluids is performed differently to conventional Newtonian fluids. During radial flow regime, Non-Newtonian fluids exhibit a pressure derivative curve which is not horizontal but rather inclined. As shown by [12], the smaller the value of *n* (flow behavior index) the more inclined is the infinite-acting pressure

A partial differential equation for radial flow of non-Newtonian fluids that follow a powerlaw relationship through porous media was proposed [11]. Coupling the non-Newtonian Darcy's law with the continuity equation, they derived a rigorous partial differential

1 2 1

*P nP μ P P*

*r rr k r t*

This equation is nonlinear. For analytical solutions, a linearized approximation was also

1 *n n* 1

*P P r Gr r rr t*

1 3792 188 96681 605 . .

.

*μ k n*

*k qB*

1 2 3 12 9 1 59344 10

*n n*

*t eff n c μ h G*

*t*

*n*

12

*H*

*eff*

*c n*

/ ( )/ *n n n eff*

(2)

*n*

( )/

(1)

(3)

(4)

2

$$P\_{DNN} = \frac{\Lambda P}{141.2 \left(96681.605\right)^{1-n} \left(\frac{qB}{h}\right)^n \frac{\mu\_{eff} r\_w^{1-n}}{k}}\tag{5}$$

$$t\_{\rm DNN} = \frac{t}{Gr\_w^{3-n}}\tag{6}$$

$$P\_{DN} = \frac{k \text{ } h\Delta P}{141.2q\mu\_N B} \tag{7}$$

$$t\_{DN} = \frac{0.0002637k \text{ t}}{\phi \mu\_N c\_t r\_w^2} \tag{8}$$

$$r\_D = \frac{r}{r\_w} \tag{9}$$

Where suffix *N* indicates Newtonian and suffix *NN* indicates non-Newtonian. The dimensionless well pressure analytical solution in the Laplace space domain for the case of a well producing a pseudoplastic non-Newtonian fluid at a constant rate from an infinite reservoir is given in reference [11]:

$$\overline{P}\_{D}\left(\hat{s}\right) = \frac{\mathsf{K}\_{\upsilon}\left(\boldsymbol{\beta}\sqrt{\tilde{\boldsymbol{s}}}r\_{D}^{\mathcal{N}\_{\beta}}\right) + s\sqrt{\tilde{\boldsymbol{s}}}\mathsf{K}\_{\beta}\left(\boldsymbol{\beta}\sqrt{\tilde{\boldsymbol{s}}}\right)}{\tilde{s}\left(\sqrt{\tilde{\boldsymbol{s}}}\mathsf{K}\_{\beta}\left(\boldsymbol{\beta}\sqrt{\tilde{\boldsymbol{s}}}\right) + \tilde{s}\mathsf{C}\_{D}\left[\boldsymbol{K}\_{\upsilon}\left(\boldsymbol{\beta}\sqrt{\tilde{\boldsymbol{s}}}\right) + s\sqrt{\tilde{\boldsymbol{s}}}\mathsf{K}\_{\beta}\left(\boldsymbol{\beta}\sqrt{\tilde{\boldsymbol{s}}}\right)\right]\right)}\tag{10}$$

Being = 2/(3-*n*) and = (1-*n*)/(3*-n*).

The dimensionless pressure derivative during radial flow regime is governed by:

$$\left(t\_D \stackrel{\*}{\*} P\_D \stackrel{\cdot}{)}\_{r \text{NN}} = 0.5 t\_{D \text{NN}}^{\alpha} \tag{11}$$

[12] presented the following expression to estimate the permeability,

$$\frac{k}{t^{\mu}\_{eff}} = \left[ 0.5 \frac{t\_r^{\alpha}}{\mathcal{C}^{\alpha} \left( t^\* \,\Delta P^\circ \right)\_r} \frac{\left( 2 \pi h \right)^{\eta \left( \alpha - 1 \right)} r\_w^{\left( 1 - \eta \right) \left( 1 - \alpha \right)}}{q^{\eta \left( \alpha - 1 \right) - \alpha}} \right]^{\frac{1}{1 - \alpha}} \tag{12}$$

where 2 α 0 1486 0 178 0 3279 . .. *n n*

being *n* the flow behavior index which may be found from the slope of the pressure derivative curve during radial flow regime. [12] also introduced another expressions and correlations to find permeability, skin factor and wellbore storage coefficient using the maximum point (peak) found on the pressure derivative curve during wellbore storage effects which are not shown here. The point of intercept between the early unit-slope line and radial flow regime is used to estimate wellbore storage:

$$t\_i = \frac{\left(3.13e^{-1.85n}\right)\mathbb{C}}{\left(2\pi h\right)^n} \frac{\mu\_{eff}}{k} \left(\frac{q}{r\_w}\right)^{n-1} \tag{13}$$

Parameters in both Equations 11 and 12 are given in CGS (centimeters, grams, seconds) units.

[1] presented more practical expressions for the determination of both permeability and skin factor:

$$\frac{k}{\mu\_{eff}} = \left[ 70.6 \left( 96681.605 \right)^{(1-\alpha)(1-\eta)} \left( \frac{0.0002637 t\_r}{n \phi \mathbf{c}\_t} \right)^{\alpha} \left( \frac{qB}{h} \right)^{n-\alpha\left(n-1\right)} \left( \frac{1}{\left(t^\* \ \Delta P' \right)\_r} \right) \right]^{\frac{1}{\left[1-\alpha\right]}} \tag{14}$$

Where is the slope of the pressure derivative curve and is defined by:

$$\alpha = \frac{1 - n}{3 - n} \tag{15}$$

Transient Pressure and Pressure Derivative Analysis for Non-Newtonian Fluids 159

$$s\_{r\text{NNN}} = \frac{1}{2} \left( \frac{\left(\Delta P\right)\_{r\text{NN}}}{\left(t^\* \,\Delta P'\right)\_{r\text{NN}}} - \frac{1}{a} \left| \left(\frac{t\_{r\text{NN}}}{G \,\,r\_w^{3-n}}\right)^a \right. \tag{16}$$

#### **4. Well pressure behavior in non-Newtonian/Newtonian interface**

158 New Technologies in the Oil and Gas Industry

= 2/(3-*n*) and

Being

units.

factor:

Where  *D*

*P s*

where 2 α 0 1486 0 178 0 3279 . .. *n n*

= (1-*n*)/(3*-n*).

[12] presented the following expression to estimate the permeability,

*tP t* (11)

1

(10)

(12)

(13)

1

(14)

(15)

1 1

*α n α n α α*

1

1 1 1 1

*β D ν β*

*s sK β s sC K β s s sK β s*

*K β sr s sK β s*

1 *β ν β D*

\*' . 0 5 *α D D DNN rNN*

2

being *n* the flow behavior index which may be found from the slope of the pressure derivative curve during radial flow regime. [12] also introduced another expressions and correlations to find permeability, skin factor and wellbore storage coefficient using the maximum point (peak) found on the pressure derivative curve during wellbore storage effects which are not shown here. The point of intercept between the early unit-slope line

1 85 1 3 13

. . *n n eff*

*k r πh*

Parameters in both Equations 11 and 12 are given in CGS (centimeters, grams, seconds)

[1] presented more practical expressions for the determination of both permeability and skin

*k t qB*

*eff t r*

*μ nc h t P*

*α*

is the slope of the pressure derivative curve and is defined by:

1 1 0 0002637 1 70 6 96681 605 . . . \* '

1 3 *n*

*n*

*α n α n α α n r*

*r w*

The dimensionless pressure derivative during radial flow regime is governed by:

*α n α α eff r k t πh r*

*i n w e C μ q t*

2

0 5. \* '

and radial flow regime is used to estimate wellbore storage:

*μ Ct P q*

In many activities of the oil industry, engineers have to deal with completion and stimulation treatment fluids such as polymer solutions and some heavy crude oils which obey a non-Newtonian power-law behavior. When it is required to conduct a treatment with a non-Newtonian fluid in an oil-bearing formation, this comes in contact with conventional oil which possesses a Newtonian nature. This implies the definition of two media with entirely different mobilities. If a pressure test is run in such a system, the interpretation of data from such a test through the use of conventional straight-line method may be erroneous and may not provide a way for verification of the results obtained. Then, [13] proposed a solution for the system sketched in Figure 3 which was solved numerically by [17].

[15] presented for the first time the pressure derivative behavior for the mentioned system, Figure 4. Notice in that plot that the pressure derivative shows an increasing slope as the flow behavior index decreases. Also, the derivative has no slope during infinite-acting Newtonian behavior, as expected.

During the non-Newtonian region, region 1 in Figure 3, Equations 13 to 15 work well. For the Newtonian region, region 2, the permeability and skin factor are estimated with the equations presented by Tiab (1993) as:

**Figure 3.** Composite non-Newtonian/Newtonian radial reservoir

**Figure 4.** Dimensionless pressure derivative behavior for *ra* = 200 ft. Case Non-Newtonian pseudoplastic

$$k\_2 = \frac{70.6q\mu\_N B}{h(t^\* \ \Delta P')\_r} \tag{17}$$

$$s\_2 = \frac{1}{2} \left[ \left( \frac{\Delta P}{t^\* \ \Delta P^\circ} \right)\_{r2} - \ln \left( \frac{k\_2 t\_{r2}}{\phi \mu\_N c\_t r\_w^2} \right) + 7.43 \right] \tag{18}$$

Suffix 2 denotes the non-Newtonian region.

[15] also found an expression to estimate the non-Newtonian permeability using the time of intersection of the non-Newtonian and Newtonian radial lines, *tiN\_NN*:

$$k = \left[ \left\langle \left( \frac{H}{12} \right) \left( 9 + \frac{3}{n} \right)^{\mathrm{n}} \left( 1.59344 \times 10^{-12} \,\mathrm{\phi} \right)^{(1-\mathrm{n})/2} \left( 96681.605 \frac{hr\_w}{qB} \right)^{\mathrm{l}-\mathrm{n}} \right\rangle^{\mathrm{l}-\mathrm{l}/\mathrm{a}} \frac{\mu\_{\mathrm{N}}^{\mathrm{l}/\mathrm{a}} \phi\_{\mathrm{l}} r\_w^2 n}{0.0002637 t\_{\mathrm{i}\mathrm{N}\\_\mathrm{NN}}} \right]^{\mathrm{l}/2} \tag{19}$$

The radius of the injected non-Newtonian fluid bank is calculated using the following correlation (not valid for *n*=1), obtained from reading the time at which the pressure derivative has its maximum value:

$$r\_a = \left[\frac{G\left(0.2258731n^3 - 0.2734284n^2 + 0.5064602n + 0.5178275\right)^{1/\alpha}}{t\_{MAX}}\right]^{1/\alpha} \tag{20}$$

[13] found that the radius of the non-Newtonian fluid bank can be found using the radius investigation equation proposed by [10]:

160 New Technologies in the Oil and Gas Industry

0.1

pseudoplastic

1.E+03 1.E+04 1.E+05 1.E+06 1.E+07 1.E+08 1.E+09 1.E+10

*n* 1 0.8 0.6 0.4 0.2

2

intersection of the non-Newtonian and Newtonian radial lines, *tiN\_NN*:

*Gn n n*

*s*

Suffix 2 denotes the non-Newtonian region.

derivative has its maximum value:

*a*

*r*

*t DNN*

70 6. ( \* ') *N r*

*qμ B k*

*P k t*

*t P μ c r*

[15] also found an expression to estimate the non-Newtonian permeability using the time of

12 0 0002637

*H hr μ cr n k*

. . .

The radius of the injected non-Newtonian fluid bank is calculated using the following correlation (not valid for *n*=1), obtained from reading the time at which the pressure

*n qB t*

.2258731 .2734284 .5 646 2 .5178275 *α n*

*MAX*

*t*

1 3 3 2 0 0 00 0 0

1 7 43

ln . \* '

*r N tw*

1 2 1 1 1 1 2 1 2 3 12 9 1 59344 10 96681 605

*α n n α n w N tw*

2 2

*r*

*ht P* (17)

/ / / /

(18)

\_

1

/

*irN NN*

(19)

(20)

**Figure 4.** Dimensionless pressure derivative behavior for *ra* = 200 ft. Case Non-Newtonian

2

2 2 2

1

*t D\*P D'*

10

$$r\_a = \left[ \Gamma \left( \frac{2}{3-n} \right) \right] \bigvee^{(n-1)} \left[ \frac{\left( 3-n \right)^2 t}{G} \right] \bigvee^{(3-n)} \tag{21}$$

where *t* is the end time of the straight line found on a non-Newtonian Cartesian graph of *P* vs. *t*1-*n*/3-*n*.

Later, [16] found that Equations 13, 14, 15 and 22 also worked for dilatant systems. This is the case when 2 < *n* < 1. The pressure derivative behavior is given in Figure 5. Notice that for this case the slope decreases as the flow behavior index increases. For dilatant-Newtonian interface the position of the front obeys the following equation:

**Figure 5.** Dimensionless pressure derivative behavior for *ra* = 200 ft. Case Non-Newtonian dilatant

**Example 1.** A constant-rate injection test for a well in a closed reservoir was generated by [13] with the data given below. It is required to estimate the permeability and the skin factor in each area and the radius of injected non-Newtonian fluid bank.

n = 0.6

**Solution**. The log-log plot of pressure and pressure derivative against injection time is given in Figure 6. Suffix 1 and 2 indicate the non-Newtonian and Newtonian regions, respectively. From Figure 6 the following information was read:

First, is evaluated with Equation 15 to be 0.17 and a value of 100.4 md was found with Equation 14 for the non-Newtonian effective fluid permeability. Equation 4 is used to find an effective viscosity of 0.06465 cp(s/ft)*n*-1. Then, the skin factor in the non-Newtonian region is found with Equation 16 to be 179.7.

**Figure 6.** Pressure and pressure derivative for example 1

A value of 6.228x10-5 hr/(ft3-*n*) was found for parameter *G* using Equation 3. This value is used in Equation 24 to find the distance from the well to the non-Newtonian fluid bank. This resulted to be 120 ft.

Equations 17 and 18 were used to estimate permeability and skin factor of the Newtonian zone. They resulted to be 100 md and 4.5.

Using a time of 0.0008 hr which corresponds to the intersect point formed between the non-Newtonian and Newtonian radial flow regime lines in Equation 19, a non-Newtonian effective fluid permeability of 96 md is re-estimated. [13] obtained a permeability of the non-Newtonian zone of 101 md and *ra* = 116 ft from conventional analysis.


**Author details**

Surendra Prasad Tripathi1 \* † , Surya Pratap Singh Somvanshi2†, Shyam Ranjan Kumar Singh3† and Anupam Mishra4†

1 KVK - Mandsaur, RVS Agriculture University, Gwalior, Madhya Pradesh, India

2 KVK - Hamirpur, Banda University of Agriculture and Technology University, Banda, Uttar Pradesh, India

3 ICAR, ATARI, Zone-IX, Jabalpur, Madhya Pradesh, India

4 Central Agricultural University, Imphal, Manipur, India

\*Address all correspondence to<EMAIL_ADDRESS>
† All authors equally contributed.

© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**3. Visual Evoked Potentials (VEPs)**

PD and parkinsonism are associated with a variety of visual signs and symptoms summarized in table 2.

Table 2. Abnormal visual symptoms in PD

presentation in a tertiary clinic, not later than 1 year from the onset of dementia [5]. Supportive elements for the diagnosis came from Clinical Assessment of Fluctuations (CAF) scale, polysomnography (PSG) and Mayo Sleep Questionnaire for the assessment of REM sleep Behaviour Disorder (RBD). CAF is a neuropsychological test [6] able to evidence, on the basis of patient and caregivers interviews, the presence of fluctuating consciousness. The questionnaires are able to discriminate 85% of DLB patients, as confirmed by autopsy [7]. Cognitive fluctuations are considered a clinical feature typical of DLB, described in 70-80%

PD and parkinsonism are associated with a variety of visual signs and symptoms

of these patients, only in 14-20% of AD patients and in 15-30% of VaD subjects [8].

**OCULAR ASPECT CHANGE IN PD REFERENCES**

VISION BLURRED FOR COLOURED STIMULI/PROGRESSIVE DETERIORATION

SLOWER THAN NORMAL

FIELD DEFECTS

HYPOMETRIA

REDUCED NOT OBSERVED

REDUCED

LONGER LATENCY

WITH L-DOPA

AMPLITUDE

IMPAIRED

Table 2. Abnormal visual symptoms in PD

SEVERE IMPAIRMENT

POOR, ESPECIALLY AT LOW CONTRAST INCREASE IN GLAUCOMATOSE VISUAL

AFFECTED EARLY IN DISEASE PROCESS ABNORMAL IN SOME PATIENTS

REDUCED AMPLITUDE OF b WAVE with

INCREASED LATENCY AND REDUCED

CHRONIC IN 30-60% TREATED CASES

PHOTIC E SCOTOPIC STIMULI REDUCED AMPLITUDE, DELAYED P50 DELAYED P100, CHANGING TO NORMAL Repka et al., 1996 Bayer et al., 2002 Price at al., 1992/ Diederich et al., 2002

Shibasaki et al., 1999 Crawford et al., 1989 Bares et al., 2003 Shibasaki et al., 1999

Garland et al., 1952 Garland et al., 1952

Biousse et al., 2004 Miceli et al., 1991

Gottlob et al., 1987 Gottlob et al., 1987 Bodis-Wallner et al.,

Sartucci et al., 2006

Davidsdottir et al., 2005 Trick et al., 1994 Diederich et al., 2005

1982

**3. Visual Evoked Potentials (VEPs)**

summarized in table 2.

**EYE MOVEMENTS**  SACCADIC GAZE SACCADIC EYE MOVEMENT SMOOTH PURSUIT OPTOKINETIC NYSTAGMUS

**BLINK REFLEX**  FREQUENCY HABITUATION

**VEP**  FLASH ERG PATTERN ERG CORTICAL VEP CHROMATIC VEP

PUPIL REACTIVITY CONTRACTION AMPLITUDE LIGHT REFLEX

**COMPLEX VISUAL FUNCTION**  VISUO-SPATIAL ORIENTATION VISUAL

HALLUCINATIONS

**PRIMARY FUNCTION**  VISUAL ACUITY VISUAL FIELD COLOR VISION

Recent epidemiological studies have shown an association between visual impairments and visual hallucinations in patients with PD [9]. Neuropsychological studies have revealed visuoperceptual impairments in PDD and DLB patients with visual hallucinations [10]. Additionally, recent radiological studies have demonstrated decreased blood flow in the posterior temporal and occipital regions in hallucinatory PD and DLB patients [11]. Taking these findings together, it is possible to speculate that visual information processing functions are selectively impaired in DLB and PDD.

Impairment of achromatic as well as chromatic vision in PD has been extensively proven using clinical, psychophysiological and electrophysiological methods (ERGs and VEPs) and attributed to dopaminergic deficiency at the retina level.

Some studies demonstrated a significant difference between PD patients and well matched control subjects in the amplitude of VEP, of flash (ERG) and pattern electroretinogram (PERG: retinal response evoked by viewing an alternating checkerboard or grating) [12]. The VEP, PERG and flash ERG originate from different parts of the retina and central nervous system and reflect different physiological processes. The changes in these potentials in PD may reflect the widespread nature of the biochemical disorder affecting both retina and central nervous system. Indeed PD patients have also been shown to have abnormal auditory evoked potentials [13]. Abnormal VEPs were described in patients with PD: the percentage of VEP delays and the amount of latency increments detected in PD patients are dependent on the spatial frequency (that is a parameter of the stimulating pattern). The VEP latency increases as a function of increasing spatial frequency [14] in normal subjects, and our results [15] show that this latency increase is enhanced in PD and also when dopamine blockers are administered. Delayed responses, consisting of increased latencies of the P100 component evoked by patterned stimuli of degree to 7.5' elements (spatial frequency of 0.5 to 4 cycles per degree) were observed in PD patients and the delays disappeared together with clinical symptoms when L-Dopa was administered [15,16,17]. The evidence of VEP delays in PD were concomitant with the identification of dopaminergic cells (amacrine and horizontal cells) in the retina, both evidences reciprocally supporting the idea that the cause of delays was dependent on retinal dopamine cell deficiencies. In these studies retinal and occipital visual evoked potentials and event-related potentials (P300) have been recorded in normal human subjects before and after the administration of the dopaminergic receptor antagonist, haloperidol, and/or the dopaminergic precursor L-DOPA. The data show that either retinal or occipital visual potentials and P300 are delayed by haloperidol. These findings are consistent with the hypothesis that haloperidol in healthy subjects mimics the electrophysiological abnormalities observed in PD. On the other hand, L-Dopa does not generally modify these latencies in controls, while it is known to decrease the same parameters in PD patients. This is in accord with the involvement of a specific mechanism in the recovery observed in PD patients during L-Dopa therapy. Data confirm that the alterations of visual and cognitive potentials observed in PD are closely related to the impairment of dopaminergic transmission. The results of our study [15] on haloperidol administration in non-PD patients showed that this dopamine receptor blocking drug increased the latency of VEPs obtained with 2 and 4 cpd stimuli, while the effect on 0.5 cpd and 1 cpd VEPs was less consistent. This finding supports the hypothesis that dopamine modifies the processing of VEPs by acting at the synaptic level. The specific sensitivity of VEP changes to the spatial frequency of stimulation in PD and haloperidol treated subjects, which is evident in our results, might suggest that the VEP abnormalities found in our study

Is There a Place for Clinical Neurophysiology Assessments in Synucleinopathies? 305

In an event related potential (ERP) the P300 is a positive deflection peaking at approximately 300 ms after a stimulus. It is supposed to be an endogenous response, mainly depending on the processing of stimulus context, involving registration, evaluation and memory of stimuli, and categorization (decision/closure) and impinging on attention and arousal [28]. P300 can reliably be elicited with relatively simple paradigms, such as the "oddball paradigm", which requires the detection of a rare ("target") stimulus within a train of frequent irrelevant "non target" stimuli. Other complex paradigms include the administration of multiple stimuli, dichotic stimuli, multisensory modalities, in order to evaluate responses evoked from anterior brain regions. We recently performed a P300 study on patients with DLB in comparisons with patients with AD matched for dementia severity and age and with age matched control subjects [29] to look for differences of P300 responses in the two dementia subtypes and for possible correlations between P300 recordings and

P300 responses were recorded with Ag/AgCl electrodes from 19 derivations corresponding to Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, T6, Cz, Fz, Pz positions of the 10-20 International System with supplementary A1 and A2 derivations. Care was taken to avoid recordings if variations of body temperature, recent food ingestion, or previous-night sleep disturbances were present [30]. A classical auditory "oddball" paradigm was used. The stimuli were 500 Hz and 1000 Hz tones, designated as the "non-target" and "target" stimuli, respectively, and delivered by STIM System Headphones. Patients were instructed to count only "target" stimuli, aloud in a preliminary trial and mentally in subsequent trials. The presentation ratio of "non target/target" tones for training was 4/1-8/1 and during recording purposes 5/1. The intensity of the tone was 75 dBnHL, the duration of the stimulus was 150 ms (rise-fall and the plateau times 5 and 140 ms, respectively). The presentation rate was random with a minimum inter-stimulus interval of 1.1 seconds and a maximum interval of 4 seconds. Digital filters was set at 0.15 Hz and 100 Hz, and averaged with a dwell time of 0.5 ms, 2000 Hz sampling rate, 100 ms of pre-stimulus baseline recording. An artifact rejection system was calibrated on four supplementary derivations placed on eyebrows and inferior orbital ridges; the rejection system blocked the acquisition when eye movement exceeded 100 uV. As the mean reference is known to distort P300 distribution one earlobe was used as online reference, with offline averaging with the other

In each recording session, the correspondence between the counted and delivered stimuli was checked as described in previous studies [32] and stored on the hard disk. Sets where two or more targets had not been recognized or sets impaired by attention defects or false

In each patient and control 120 responses to non-target and target stimuli were averaged in a single Final Average (FA). Four Sub-Averages (SA) of only 30 responses to target stimuli were preliminarily obtained in order to assess reliability of P300 among the recording sessions. Finally, inter-subject Grand Averages (GA) were obtained in DLB, AD and control groups. N100, N200, and P200 were detected at the Fz, Cz and Pz electrode for each subject separately. Peak latencies of each component were measured from stimulus onset to the point of maximum voltage in the range of 50-150 ms and 150-250 ms respectively. P300 was identified, in a time window of 300-500 ms, according to the operating definition based on

EEG, as abnormal EEG variability was described in DLB [5].

**4. P 300 abnormalities**

earlobe [31].

recognitions were discarded from analysis.

are dependent on the impairment of dopaminergic neural structures which regulate spatial frequency sensitivity. VEP findings were robustly confirmed in studies performed in animal models of PD [18, 19]. Despite the interest for the finding, only few confirmative studies were provided [15-17], most studies come from few laboratories devoted to this experimental approach. Guidelines for the use of VEPs in clinical practice only rarely suggested a role of VEPs in PD studies and VEPs were finally confined to the assessment of Multiple Sclerosis. Although the increased latency of the VEP in multiple sclerosis has in general been attributed to demyelination in the visual pathways, other mechanisms such as humoral factors, synaptic malfunction or changes in dendritic potentials may play a part. Such mechanisms may also be relevant to central nervous system disorders other than multiple sclerosis which have abnormal VEPs. Although the major clinical manifestations of PD involve the motor systems and the responsible pathology is located in the basal ganglia, there is evidence of more widespread disease, both pathologically, electrophysiologically and clinically. Only two studies explored the possible use of VEPs for the assessment of DLB [20,21] yet no comparison were presented with other forms of dementia. In MSA the visual system is believed to be spared and dopamine deficiency has been hypothesized to be less pronounced than in PD [22], even though the data in the literature are scarce and not unanimous and nothing on retinal dopamine content has been reported. Little information is available on VEPs and PERGs in MSA patients [22]. The main interest for studying responses elicited by patterns with pure chromatic contrast is that they allow recording of specific responses from colour-opponent pathways, anatomically and physiologically distinct from the achromatic ones at the retinal as well as the geniculate and cortical levels. A more recent study [23] showed that PERGs are virtually unaffected in MSA, whereas in early PD they are clearly impaired, suggesting different pathogenic retinal mechanisms and a useful simple tool for distinguishing MSA from PD. The strongest objection against the use of VEPs for the assessment of synucleinopathies derived from the technical constraints of VEP recordings: VEPs are altered by abnormalities of optic nerve and visual pathways, VEPs recordings require the adequate collaboration of patients who must focus attention on stimuli [24-26], VEP variable (amplitude, latency) are dependent on laboratories settings and must be adjusted according to each laboratory statistics of distribution.

The characteristic of VEP cannot be simply shared by different laboratories and differences in equipments might sustain variability which are far wider than variability observed in patient and control populations.

With the introduction of digital and led stimulating screen this condition worsened rather than improving [27].

In the age range of AD, DLB and PDD optic and visual abnormalities (cataracts, maculopathies, retinopathies, ischemic lesions) are frequent and might mislead possible diagnoses. In DLB, fluctuations of cognition (i.e. defective attention and collaboration to the task) are common and might impair the diagnostic yield of VEP recordings. It has been suggested that the discrepancies between different reports on VEP in Parkinson's disease may be due to the greater sensitivity of grating patterns compared to checkerboard patterns and, if so, this might in part account for our normal PD VEP latencies. The grating subtense used is about one third of that for the checkerboard, and the retinal field stimulated is predominantly foveal for the grating whereas for the checkerboard it extends beyond the perimacula. This could explain the observed differences rather than the pattern form, per se. We suggest that VEP recordings might still represent a "niche" research tool, but do not provide sufficient robustness in order to constitute a biomarker.