Influence of sports on cortical excitability in patients with spinal cord injury: a TMS study.

Background: Patients with spinal cord injury (SCI) show abnormal cortical excitability that might be caused by deafferentation. We hypothesize a reduced short-interval intracortical inhibition preceding movement in patients with SCI compared with healthy participants. In addition, we expect that neuroplasticity induced by different types of sports can modulate intracortical inhibition during movement preparation in patients with SCI. Methods: We used a reaction test and paired-pulse transcranial magnetic stimulation to record cortical excitability, assessed by measuring amplitudes of motor-evoked potentials in preparation of movement. The participants were grouped as patients with SCI practicing wheelchair dancing (n = 7), other sports (n = 6), no sports (n = 9), and healthy controls (n = 24). Results: There were neither significant differences between healthy participants and the patients nor between the different patient groups. A non-significant trend (p = .238), showed that patients engaged in sports have a stronger increase in cortical excitability compared with patients of the non-sportive group, while the patients in the other sports group expressed the highest increase in cortical excitability. Conclusion: The small sample sizes limit the statistical power of the study, but the trending effect warrants further investigation of different sports on the neuroplasticity in patients with SCI. It is not clear how neuroplastic changes impact the sensorimotor output of the affected extremities in a patient. This needs to be followed up in further studies with a greater sample size.

Movement Termination of Slow-Wave Sleep-A Potential Biomarker?

The duration of slow-wave sleep (SWS) is related to the reported sleep quality and to the important variables of mental and physical health. The internal cues to end an episode of SWS are poorly understood. One such internal cue is the initiation of a body movement, which is detectable as electromyographic (EMG) activity in sleep-electroencephalography (EEG). In the present study, we characterized the termination of SWS episodes by movement to explore its potential as a biomarker. To this end, we characterized the relation between the occurrence of SWS termination by movement and individual characteristics (age, sex), SWS duration and spectral content, chronotype, depression, medication, overnight memory performance, and, as a potential neurological application, epilepsy. We analyzed 94 full-night EEG-EMG recordings (75/94 had confirmed epilepsy) in the video-EEG monitoring unit of the EpiCARE Centre Salzburg, Austria. Segments of SWS were counted and rated for their termination by movement or not through the visual inspection of continuous EEG and EMG recordings. Multiple linear regression was used to predict the number of SWS episodes that ended with movement by depression, chronotype, type of epilepsy (focal, generalized, no epilepsy, unclear), medication, gender, total duration of SWS, occurrence of seizures during the night, occurrence of tonic-clonic seizures during the night, and SWS frequency spectra. Furthermore, we assessed whether SWS movement termination was related to overnight memory retention. According to multiple linear regression, patients with overall longer SWS experienced more SWS episodes that ended with movement (t = 5.64; p = 0.001). No other variable was related to the proportion of SWS that ended with movement, including no epilepsy-related variable. A small sample (n = 4) of patients taking Sertraline experienced no SWS that ended with movement, which was significant compared to all other patients (t = 8.00; p < 0.001) and to n = 35 patients who did not take any medication (t = 4.22; p < 0.001). While this result was based on a small subsample and must be interpreted with caution, it warrants replication in a larger sample with and without seizures to further elucidate the role of the movement termination of SWS and its potential to serve as a biomarker for sleep continuity and for medication effects on sleep.

The effects of slow wave sleep characteristics on semantic, episodic, and procedural memory in people with epilepsy.

Slow wave sleep (SWS) is highly relevant for verbal and non-verbal/spatial memory in healthy individuals, but also in people with epilepsy. However, contradictory findings exist regarding the effect of seizures on overnight memory retention, particularly relating to procedural and non-verbal memory, and thorough examination of episodic memory retention with ecologically valid tests is missing. This research explores the interaction of SWS duration with epilepsy-relevant factors, as well as the relation of spectral characteristics of SWS on overnight retention of procedural, verbal, and episodic memory. In an epilepsy monitoring unit, epilepsy patients (N = 40) underwent learning, immediate and 12 h delayed testing of memory retention for a fingertapping task (procedural memory), a word-pair task (verbal memory), and an innovative virtual reality task (episodic memory). We used multiple linear regression to examine the impact of SWS duration, spectral characteristics of SWS, seizure occurrence, medication, depression, seizure type, gender, and epilepsy duration on overnight memory retention. Results indicated that none of the candidate variables significantly predicted overnight changes for procedural memory performance. For verbal memory, the occurrence of tonic-clonic seizures negatively impacted memory retention and higher psychoactive medication load showed a tendency for lower verbal memory retention. Episodic memory was significantly impacted by epilepsy duration, displaying a potential nonlinear impact with a longer duration than 10 years negatively affecting memory performance. Higher drug load of anti-seizure medication was by tendency related to better overnight retention of episodic memory. Contrary to expectations longer SWS duration showed a trend towards decreased episodic memory performance. Analyses on associations between memory types and EEG band power during SWS revealed lower alpha-band power in the frontal right region as significant predictor for better episodic memory retention. In conclusion, this research reveals that memory modalities are not equally affected by important epilepsy factors such as duration of epilepsy and medication, as well as SWS spectral characteristics.

Shorter duration of slow wave sleep is related to symptoms of depression in patients with epilepsy.

Slow wave sleep duration and spectral abnormalities are related to both epilepsy and depression, but it is unclear how depressive symptoms in patients with epilepsy are affected by slow wave sleep duration and clinical factors, and how the spectral characteristics of slow wave sleep reflect a potential interaction of epilepsy and depression. Long-term video-EEG monitoring was conducted in 51 patients with focal epilepsy, 13 patients with generalized epilepsy, and 9 patients without epilepsy. Slow wave sleep segments were manually marked in the EEG and duration as well as EEG power spectra were extracted. Depressive symptoms were documented with the Beck Depression Inventory (BDI). At least mild depressive symptoms (BDI > 9) were found among 23 patients with focal epilepsy, 5 patients with generalised epilepsy, and 6 patients who had no epilepsy diagnosis. Slow wave sleep duration was shorter for patients with at least mild depressive symptoms (p =.004), independently from epilepsy diagnosis, antiseizure medication, age, and sex. Psychoactive medication was associated with longer slow wave sleep duration (p =.008). Frontal sigma band power (13-15 Hz) during slow wave sleep was higher for patients without epilepsy and without depressive symptoms as compared to patients without depressive symptoms but with focal epilepsy (p =.005). Depressive symptoms affect slow wave sleep duration of patients with epilepsy similarly as in patients without epilepsy. Since reduced slow wave sleep can increase the likelihood of seizure occurrence, these results stress the importance of adequate treatment for patients with epilepsy who experience depressive symptoms.

Emotional Bias among Individuals at Risk for Seasonal Affective Disorder-An EEG Study during Remission in Summer.

Emotional bias in attention and memory is well researched in depression. Patients with depression prioritize processing of negative information over positive input. While there is evidence that emotional bias exists in seasonal affective disorder (SAD) during winter, it is unclear whether such altered cognition exists also during summer. Moreover, it is unclear whether such bias affects attention, memory, or both. In this study, we investigated 110 individuals in summer, 34 of whom reported suffering from low mood during winter, according to the seasonal pattern assessment questionnaire. While the electroencephalogram was recorded, participants learned 60 emotional pictures and subsequently were asked to recognize them in an old/new task. There were no clear group differences in behavioral measures, and no brain response differences in frontal alpha power during learning. During recognition, at 100-300 ms post stimulus individuals with higher seasonality scores exhibited larger alpha power in response to negative as compared to neutral stimuli, while individuals with low seasonality scores exhibited larger alpha power in response to positive as compared to neutral stimuli. While we cannot draw conclusions whether this is an effect of attention or memory, the finding suggests that early cognitive processes are altered already during summer in individuals with increased likelihood to experience SAD during winter. Our data provide evidence for an all-year-round cognitive vulnerability in this population.

EEG-responses to mood induction interact with seasonality and age.

The EEG is suggested as a potential diagnostic and prognostic biomarker for seasonal affective disorder (SAD). As a pre-clinical form of SAD, seasonality is operationalized as seasonal variation in mood, appetite, weight, sleep, energy, and socializing. Importantly, both EEG biomarkers and seasonality interact with age. Inducing sad mood to assess cognitive vulnerability was suggested to improve the predictive value of summer assessments for winter depression. However, no EEG studies have been conducted on induced sad mood in relation to seasonality, and no studies so far have controlled for age. We recorded EEG and calculated bandpower in 114 participants during rest and during induced sad mood in summer. Participants were grouped by age and based on a seasonality score as obtained with the seasonal pattern assessment questionnaire (SPAQ). Participants with high seasonality scores showed significantly larger changes in EEG power from rest to sad mood induction, specifically in the alpha frequency range (p = 0.027), compared to participants with low seasonality scores. Furthermore, seasonality interacted significantly with age (p < 0.001), with lower activity in individuals with high seasonality scores that were older than 50 years but the opposite pattern in individuals up to 50 years. Effects of sad mood induction on brain activity are related to seasonality and can therefore be consider as potential predicting biomarkers for SAD. Future studies should control for age as a confounding factor, and more studies are needed to elaborate on the characteristics of EEG biomarkers in participants above 50 years.

Predictability of Seasonal Mood Fluctuations Based on Self-Report Questionnaires and EEG Biomarkers in a Non-clinical Sample.

Induced by decreasing light, people affected by seasonal mood fluctuations may suffer from low energy, have low interest in activities, experience changes in weight, insomnia, difficulties in concentration, depression, and suicidal thoughts. Few studies have been conducted in search for biological predictors of seasonal mood fluctuations in the brain, such as EEG oscillations. A sample of 64 participants was examined with questionnaires and electroencephalography in summer. In winter, a follow-up survey was recorded and participants were grouped into those with at least mild (N = 18) and at least moderate (N = 11) mood decline and those without self-reported depressive symptoms both in summer and in winter (N = 46). A support vector machine was trained to predict mood decline by either EEG biomarkers alone, questionnaire data from baseline alone, or a combination of the two. Leave-one-out-cross validation with lasso regularization was used with logistic regression to fit a model. The accuracy for classification for at least mild/moderate mood decline was 77/82% for questionnaire data, 72/82% for EEG alone, and 81/86% for EEG combined with questionnaire data. Self-report data was more conclusive than EEG biomarkers recorded in summer for prediction of worsening of depressive symptoms in winter but it is advantageous to combine EEG with psychological assessment to boost predictive performance.

Involvement of central sensory pathways in subjects with restless legs syndrome: A neurophysiological study.

In patients with restless legs syndrome (RLS) a motor cortical disinhibition has been reported in transcranial magnetic stimulation (TMS) studies, but the neuronal excitability in other cortical areas has been poorly explored. The aim of this study was the functional evaluation of thalamo-cortical circuits and inhibitory cortical responses in the sensory cortex in RLS. We assessed the high-frequency somatosensory evoked potentials (HF-SEP) in sixteen subjects suffering from RLS of different degrees of severity. In patients with severe or very severe RLS we found a significant desynchronization with amplitude reduction of both pre- and post-synaptic HF-SEP bursts, which suggest an impairment in the thalamo-cortical projections and in the cortical inhibitory interneurons activity, respectively. The assessment of the central sensory pathways by means of HF-SEP may shed light on the pathophysiological mechanisms of RLS.

Distance learning in higher education during COVID-19: The role of basic psychological needs and intrinsic motivation for persistence and procrastination-a multi-country study.

Due to the COVID-19 pandemic, higher educational institutions worldwide switched to emergency distance learning in early 2020. The less structured environment of distance learning forced students to regulate their learning and motivation more independently. According to self-determination theory (SDT), satisfaction of the three basic psychological needs for autonomy, competence and social relatedness affects intrinsic motivation, which in turn relates to more active or passive learning behavior. As the social context plays a major role for basic need satisfaction, distance learning may impair basic need satisfaction and thus intrinsic motivation and learning behavior. The aim of this study was to investigate the relationship between basic need satisfaction and procrastination and persistence in the context of emergency distance learning during the COVID-19 pandemic in a cross-sectional study. We also investigated the mediating role of intrinsic motivation in this relationship. Furthermore, to test the universal importance of SDT for intrinsic motivation and learning behavior under these circumstances in different countries, we collected data in Europe, Asia and North America. A total of N = 15,462 participants from Albania, Austria, China, Croatia, Estonia, Finland, Germany, Iceland, Japan, Kosovo, Lithuania, Poland, Malta, North Macedonia, Romania, Sweden, and the US answered questions regarding perceived competence, autonomy, social relatedness, intrinsic motivation, procrastination, persistence, and sociodemographic background. Our results support SDT's claim of universality regarding the relation between basic psychological need fulfilment, intrinsic motivation, procrastination, and persistence. However, whereas perceived competence had the highest direct effect on procrastination and persistence, social relatedness was mainly influential via intrinsic motivation.

Quantitative EEG biomarkers for epilepsy and their relation to chemical biomarkers.

The electroencephalogram (EEG) is the most important method to diagnose epilepsy. In clinical settings, it is evaluated by experts who identify patterns visually. Quantitative EEG is the application of digital signal processing to clinical recordings in order to automatize diagnostic procedures, and to make patterns visible that are hidden to the human eye. The EEG is related to chemical biomarkers, as electrical activity is based on chemical signals. The most well-known chemical biomarkers are blood laboratory tests to identify seizures after they have happened. However, research on chemical biomarkers is much less extensive than research on quantitative EEG, and combined studies are rarely published, but highly warranted. Quantitative EEG is as old as the EEG itself, but still, the methods are not yet standard in clinical practice. The most evident application is an automation of manual work, but also a quantitative description and localization of interictal epileptiform events as well as seizures can reveal important hints for diagnosis and contribute to presurgical evaluation. In addition, the assessment of network characteristics and entropy measures were found to reveal important insights into epileptic brain activity. Application scenarios of quantitative EEG in epilepsy include seizure prediction, pharmaco-EEG, treatment monitoring, evaluation of cognition, and neurofeedback. The main challenges to quantitative EEG are poor reliability and poor generalizability of measures, as well as the need for individualization of procedures. A main hindrance for quantitative EEG to enter clinical routine is also that training is not yet part of standard curricula for clinical neurophysiologists.

Quantitative EEG in Cognitive Neuroscience.

Quantitative electroencephalography (EEG) distinguishes itself from clinical EEG by the application of mathematical approaches and computer scientific methods [...].

Cognitive Effects of Montelukast: A Pharmaco-EEG Study.

Montelukast is a well-established antiasthmatic drug with little side effects. It is a leukotriene receptor antagonist and recent research suggests cognitive benefits from its anti-inflammatory actions on the central nervous system. However, changes in brain activity were not directly shown so far in humans. This study aims to document changes in brain activity that are associated with cognitive improvement during treatment with Montelukast. We recorded EEG and conducted neuropsychological tests in 12 asthma-patients aged 38-73 years before and after 8 weeks of treatment with Montelukast. We found no significant changes on neuropsychological scales for memory, attention, and mood. In the EEG, we found decreased entropy at follow up during rest (p < 0.005). During episodic memory acquisition we found decreased entropy (p < 0.01) and acceleration of the background rhythm (p < 0.05). During visual attention performance, we detected an increase in gamma power (p < 0.005) and slowing of the background rhythm (p < 0.05). The study is limited by its small sample size, young age and absence of baseline cognitive impairment of the participants. Unspecific changes in brain activity were not accompanied by cognitive improvement. Future studies should examine elderly patients with cognitive impairment in a double-blind study with longer-term treatment by Montelukast.

Investigating the Effects of Seizures on Procedural Memory Performance in Patients with Epilepsy.

Memory complaints are frequently reported by patients with epilepsy and are associated with seizure occurrence. Yet, the direct effects of seizures on memory retention are difficult to assess given their unpredictability. Furthermore, previous investigations have predominantly assessed declarative memory. This study evaluated within-subject effects of seizure occurrence on retention and consolidation of a procedural motor sequence learning task in patients with epilepsy undergoing continuous monitoring for five consecutive days. Of the total sample of patients considered for analyses (N = 53, Mage = 32.92 ± 13.80 y, range = 18-66 y; 43% male), 15 patients experienced seizures and were used for within-patient analyses. Within-patient contrasts showed general improvements over seizure-free (day + night) and seizure-affected retention periods. Yet, exploratory within-subject contrasts for patients diagnosed with temporal lobe epilepsy (n = 10) showed that only seizure-free retention periods resulted in significant improvements, as no performance changes were observed following seizure-affected retention. These results indicate general performance improvements and offline consolidation of procedural memory during the day and night. Furthermore, these results suggest the relevance of healthy temporal lobe functioning for successful consolidation of procedural information, as well as the importance of seizure control for effective retention and consolidation of procedural memory.

Connectivity Analysis during Rubber Hand Illusion-A Pilot TMS-EEG Study in a Patient with SCI.

Background: Bodily self-perception is an important concept for several neurological disorders, including spinal cord injury (SCI). Changing one's bodily self-perception, e.g., via rubber hand illusion (RHI), induces alterations of bottom-up and top-down pathways and with this the connectivity between involved brain areas. We aim to examine whether (1) this process can be manipulated by changing cortical excitability, (2) connectivity between relevant brain areas differ when the RHI cannot be evoked, and (3) how this projection differs in a patient with SCI. Method: We applied RHI and facilitatory theta burst stimulation (TBS) on the right primary somatosensory cortex (S1) of 18 healthy participants and one patient with incomplete, cervical SCI. During RHI, we recorded high-density electroencephalography (HD-EEG) and extracted directed and nondirected connectivity measures. Results: There is no difference in connectivity between sham and real TBS or in the effectivity of RHI. We observed a higher laterality in the patient, i.e., higher connectivity of the right and lower of the left hemisphere. Besides this, connectivity patterns do not differ between healthy participants and the patient. Conclusion: This connectivity pattern might represent a neuroplastic response in the attempt to overcome the functional impairment of the patient resulting in a similar overall connectivity pattern to the healthy participants, yet with a higher sensitivity towards RHI and a higher laterality. The cortico-cortical communication was not altered depending on whether the illusion was provoked or not; hence, the perceptory illusion could not be observed in the EEG analysis.

The effect of age and chronotype on seasonality, sleep problems, and mood.

Seasonal affective disorder has been associated with sleep problems, young age, and an evening chronotype. A chronotype refers to an individual's preference in the timing of their sleep-wake cycle, as well as the time during the sleep-wake cycle when a person is most alert and energetic. Seasonality refers to season-dependent fluctuations in sleep length, social activity, mood, weight, appetite, and energy level. Evening chronotype is more common in young adults and morning chronotype more common in the elderly. This study aimed to estimate the differential contribution of chronotype and age on seasonality. A sample of n=410 participants were included in the study. The age groups showed significantly different results according to sleep parameters, depression, anxiety, stress, seasonality, and chronotype. The oldest group (>59 years) showed the lowest scores on all of these scales. According to a path analysis, chronotype and age predict propensity for seasonality. However, sleep problems were linked to chronotype but not to age. Older adults seem to be more resistant to seasonal changes that are perceived as a problem than young and middle aged adults. Future studies would benefit from considering cultural aspects and examine seasonality, chronotype, depression, and insomnia in longitudinal designs.

Are High Frequency Oscillations in Scalp EEG Related to Age?

BACKGROUND: High-frequency oscillations (HFOs) have received much attention in recent years, particularly in the clinical context. In addition to their application as a marker for pathological changes in patients with epilepsy, HFOs have also been brought into context with several physiological mechanisms. Furthermore, recent studies reported a relation between an increase of HFO rate and age in invasive EEG recordings. The present study aimed to investigate whether this relation can be replicated in scalp-EEG. METHODS: We recorded high-density EEG from 11 epilepsy patients at rest as well as during motor performance. Manual detection of HFOs was performed by two independent raters following a standardized protocol. Patients were grouped by age into younger (<25 years) and older (>50 years) participants. RESULTS: No significant difference of HFO-rates was found between groups [U = 10.5, p = 0.429, r = 0.3]. CONCLUSIONS: Lack of replicability of the age effect of HFOs may be due to the local propagation patterns of age-related HFOs occurring in deep structures. However, limitations such as small sample size, decreased signal-to-noise ratio as compared to invasive recordings, as well as HFO-mimicking artifacts must be considered.

Automatic vs. Manual Detection of High Frequency Oscillations in Intracranial Recordings From the Human Temporal Lobe.

Background: High frequency oscillations (HFOs) have attracted great interest among neuroscientists and epileptologists in recent years. Not only has their occurrence been linked to epileptogenesis, but also to physiologic processes, such as memory consolidation. There are at least two big challenges for HFO research. First, detection, when performed manually, is time consuming and prone to rater biases, but when performed automatically, it is biased by artifacts mimicking HFOs. Second, distinguishing physiologic from pathologic HFOs in patients with epilepsy is problematic. Here we automatically and manually detected HFOs in intracranial EEGs (iEEG) of patients with epilepsy, recorded during a visual memory task in order to assess the feasibility of the different detection approaches to identify task-related ripples, supporting the physiologic nature of HFOs in the temporal lobe. Methods: Ten patients with unclear seizure origin and bilaterally implanted macroelectrodes took part in a visual memory consolidation task. In addition to iEEG, scalp EEG, electrooculography (EOG), and facial electromyography (EMG) were recorded. iEEG channels contralateral to the suspected epileptogenic zone were inspected visually for HFOs. Furthermore, HFOs were marked automatically using an RMS detector and a Stockwell classifier. We compared the two detection approaches and assessed a possible link between task performance and HFO occurrence during encoding and retrieval trials. Results: HFO occurrence rates were significantly lower when events were marked manually. The automatic detection algorithm was greatly biased by filter-artifacts. Surprisingly, EOG artifacts as seen on scalp electrodes appeared to be linked to many HFOs in the iEEG. Occurrence rates could not be associated to memory performance, and we were not able to detect strictly defined "clear" ripples. Conclusion: Filtered graphoelements in the EEG are known to mimic HFOs and thus constitute a problem. So far, in invasive EEG recordings mostly technical artifacts and filtered epileptiform discharges have been considered as sources for these "false" HFOs. The data at hand suggests that even ocular artifacts might bias automatic detection in invasive recordings. Strict guidelines and standards for HFO detection are necessary in order to identify artifact-derived HFOs, especially in conditions when cognitive tasks might produce a high amount of artifacts.

Altered response to repetitive transcranial magnetic stimulation in patients with chronic primary insomnia.

BACKGROUND: We aimed at evaluating the amplitude changes of the motor evoked potentials (MEPs) induced by of low-frequency (LF) repetitive transcranial magnetic stimulation (rTMS) over the primary motor cortex (M1) in10 patients with primary insomnia (PI) and in 10 age-matched healthy controls. METHODS: Median peak-to-peak MEP amplitudes were assessed in all subjects at three times: at baseline (T0), after the first train of a single rTMS session (T1), and after the whole rTMS procedure (T2). This consists of 20 trains of 1 Hz stimulation with 50 stimuli per train and an intertrain interval of 30 s. RESULTS: Resting motor threshold (RMT) and MEPs amplitude did not differ between the two groups at T0. A reduction of MEP size was observed at both T1 and T2 in all subjects, but this was significantly less pronounced in patients than in control subjects. CONCLUSIONS: The lack of MEP inhibition reflects an altered response to LF rTMS in patients with PI. These rTMS findings are indicative of an altered cortical plasticity in inhibitory circuits within M1 in PI. Subjects with PI exhibited an impairment of the LTD-like mechanisms induced by inhibitory rTMS, thus providing further support to the involvement of GABA neurotransmission in the pathophysiology of PI.

Prediction of Cognitive Decline in Temporal Lobe Epilepsy and Mild Cognitive Impairment by EEG, MRI, and Neuropsychology.

Cognitive decline is a severe concern of patients with mild cognitive impairment. Also, in patients with temporal lobe epilepsy, memory problems are a frequently encountered problem with potential progression. On the background of a unifying hypothesis for cognitive decline, we merged knowledge from dementia and epilepsy research in order to identify biomarkers with a high predictive value for cognitive decline across and beyond these groups that can be fed into intelligent systems. We prospectively assessed patients with temporal lobe epilepsy (N = 9), mild cognitive impairment (N = 19), and subjective cognitive complaints (N = 4) and healthy controls (N = 18). All had structural cerebral MRI, EEG at rest and during declarative verbal memory performance, and a neuropsychological assessment which was repeated after 18 months. Cognitive decline was defined as significant change on neuropsychological subscales. We extracted volumetric and shape features from MRI and brain network measures from EEG and fed these features alongside a baseline testing in neuropsychology into a machine learning framework with feature subset selection and 5-fold cross validation. Out of 50 patients, 27 had a decline over time in executive functions, 23 in visual-verbal memory, 23 in divided attention, and 7 patients had an increase in depression scores. The best sensitivity/specificity for decline was 72%/82% for executive functions based on a feature combination from MRI volumetry and EEG partial coherence during recall of memories; 95%/74% for visual-verbal memory by combination of MRI-wavelet features and neuropsychology; 84%/76% for divided attention by combination of MRI-wavelet features and neuropsychology; and 81%/90% for increase of depression by combination of EEG partial directed coherence factor at rest and neuropsychology. Combining information from EEG, MRI, and neuropsychology in order to predict neuropsychological changes in a heterogeneous population could create a more general model of cognitive performance decline.