Basic understanding of human cognition It is the current hypothesis that<unk>  represent the basic steps of cognition and neural information processing in the brain, but there is still much research that needs to be done to cement this theory. Koenig, Lehmann et al. 2002 This study investigated<unk> variance across normal humans of varying ages. It showed a "lawful, complex evolution with age" with spikes in mean<unk> duration around ages 12, 16, 18, and 40–60 years, suggesting that there is significant cerebral evolution occurring at those ages. As for the cause of this, they hypothesized that it was due to the growth and restructure of neural pathways, :"In studies on the micro-architecture of developing brain tissue, it has been observed that after an initial excess of relatively unorganized synaptic connections, the number of synapses gradually decreased, while the degree of organization of the connections increased (Huttenlocher, 1979; Rakic et al., 1986). It is thus more likely that the observed changes in<unk> profile result form the elimination of non-functional connections rather than from the formation of new ones. Another possible relation of the present results with neurobiological processes comes from the observation that with increasing age, asymmetric<unk>  diminish, while symmetric<unk>  increase. Assuming that asymmetric<unk>  result from predominantly unilateral brain activity, while symmetric<unk>  indicate predominantly bilateral activity, the observed effects may be related to the growth of the corpus callosum, which continues until late adolescence (e.g., Giedd et al., 1999)." Van De Ville, Britz, and Michel, 2010 In a study conducted by researchers in Geneva, the temporal dynamics and possible fractal properties of<unk>  were analyzed in normal human subjects. Since<unk>  are a global topography, but occur on such small time scales and change so rapidly, Van De Ville, Britz, and Michel hypothesized that these "atoms of thoughts" are fractal-like in the temporal dimension. That is, whether scaled-up or scaled-down, an<unk>  is itself a composition of<unk> . This hypothesis was initially illuminated by the strong correlation between the rapid time scale and transience of<unk>  and the much slower signals of a resting state fMRI. :"The connection between<unk>  and fMRI resting state networks (RSNs) was established by convolving the time courses of the occurrence of the different<unk>  with the hemodynamic response function (HRF) and then using these as regressors in a general linear model for conventional fMRI analysis. Because the HRF acts as a strong temporal smoothing filter on the rapid<unk> -based signal, it is remarkable that statistically significant correlations can be found. The fact that this smoothing did not remove any information-carrying signal from the<unk> sequence and that furthermore the original<unk> sequences and the regressors show the same relative behavior at temporal scales about two orders of magnitude apart suggests that the time courses of the<unk>  are scale invariant." This scale-invariant dynamic is the strongest characteristic of a fractal, and since<unk>  are indicative of global neuronal networks, it is justifiable to conclude that these<unk>  exhibit temporally monofractal (one-dimensional fractal) behavior. From here we can see the possibility that fMRI, which is also a global topography measure, is possibly just a scaled-up manifestation of its<unk> , and thus further supports the hypothesis that<unk>  are the fundamental unit of one's global cognitive processing. Psychological pathologies Comparing the<unk> classes between controls and those with psychosis has yielded important results, suggesting that the basic resting-state of those with psychosis is irregular. This implies that before any information is processed or created, it is bound to the dynamics of the irregular<unk> sequencing. Although<unk> analysis has great potential to help understand the basic mechanisms of some neurological diseases, there is still much work and understanding that needs to be developed before it can be a widely accepted diagnostic. Schizophrenia Numerous studies have investigated the temporal dynamics of<unk>  in people with schizophrenia. In the first study comparing the temporal dynamics of the<unk>  in those with schizophrenia against healthy controls, Koenig and Lehmann reported that those with schizophrenia tend to spend too much time in<unk> Class A compared to controls. However, other studies in schizophrenia research have suggested a different picture. A meta-analysis comprising studies from 1999 to 2015 revealed that<unk> class C occurred more frequently and for longer durations in those with schizophrenia than controls, while<unk> class D occurred less frequently and for shorter durations. These results were also confirmed by a later meta-analysis. Similar abnormalities were reported in a study with adolescents with 22q11.2 deletion syndrome, a population that has a 30% risk of developing psychosis. Classes C and D abnormalities were also found in unaffected siblings of those with schizophrenia, which prompted the authors to suggest that dynamics of<unk>  C and D are a candidate endophenotype for schizophrenia. Panic disorder In July 2011, Dr. Koenig collaborated with researchers from Kanazawa University in Japan, and others from the University of Bern in Switzerland, to do a<unk> analysis on people with panic disorder (PD). They found that these people spent too much time in the same right-anterior to left-posterior<unk> as in the schizophrenia studies. This suggests temporal lobe malfunction, which has been reported in fMRI studies of those with PD; they spent an average of 9.26 milliseconds longer in this<unk> than did control subjects. These aberrant<unk> sequences are very similar to those in the schizophrenia study, and as anxiety is commonly found in schizophrenia, it may indicate a strong correlation between different severities of neurological pathologies and a person's<unk> sequence. Sleep analysis In 1999, Cantero, Atienza, Salas, and Gómez studied alpha rhythms in normal human subjects in 3 states: eyes closed/relaxing, drowsiness at sleep onset, and REM sleep. They found that the mean determined<unk> classes were different amongst consciousness states on 3 different parameters. * Mean<unk> duration was longer during eyes-closed relaxation than the other 2 states * Total number of<unk>  per second was greatest during drowsiness at sleep onset * The number of classes determined was also greatest during drowsiness at sleep onset This study illuminates the complexity of brain activity and<unk>  dynamics. The data suggest that "alpha (wave) activity could be indexing different brain information in each arousal state." Furthermore, they suggest that the alpha rhythm could be the "natural resonance frequency of the visual cortex during the waking state, whereas the alpha activity that appears in the drowsiness period at sleep onset could be indexing the hypnagogic imagery self-generated by the sleeping brain, and a phasic event in the case of REM sleep." Another claim is that longer periods of stable brain activity may be handling smaller amounts of information processing, and thus few changes in<unk> , while shorter, less-stable brain activity may reflect large amounts of different information to process, and thus more<unk> changes.
EEG microstates
Sleep onset latency
Seizure threshold
Seizure types
alt of Sleepwalking
Microsleep
Absence seizure
Steady state visually evoked potential