Model selection for spectral parameterization.

Neurophysiological brain activity comprises rhythmic (periodic) and arrhythmic (aperiodic) signal elements, which are increasingly studied in relation to behavioral traits and clinical symptoms. Current methods for spectral parameterization of neural recordings rely on user-dependent parameter selection, which challenges the replicability and robustness of findings. Here, we introduce a principled approach to model selection, relying on Bayesian information criterion, for static and time-resolved spectral parameterization of neurophysiological data. We present extensive tests of the approach with ground-truth and empirical magnetoencephalography recordings. Data-driven model selection enhances both the specificity and sensitivity of spectral and spectrogram decompositions, even in non-stationary contexts. Overall, the proposed spectral decomposition with data-driven model selection minimizes the reliance on user expertise and subjective choices, enabling more robust, reproducible, and interpretable research findings.

Neurochemical organization of cortical proteinopathy and neurophysiology along the Alzheimer's disease continuum.

INTRODUCTION: Despite parallel research indicating amyloid-ß accumulation, alterations in cortical neurophysiological signaling, and multi-system neurotransmitter disruptions in Alzheimer's disease (AD), the relationships between these phenomena remains unclear. METHODS: Using magnetoencephalography, positron emission tomography, and an atlas of 19 neurotransmitters, we studied the alignment between neurophysiological alterations, amyloid-ß deposition, and the neurochemical gradients of the cortex. RESULTS: In patients with mild cognitive impairment and AD, changes in cortical rhythms were topographically aligned with cholinergic, serotonergic, and dopaminergic systems. These alignments correlated with the severity of clinical impairments. Additionally, cortical amyloid-ß plaques were preferentially deposited along neurochemical boundaries, influencing how neurophysiological alterations align with muscarinic acetylcholine receptors. Most of the amyloid-ß-neurochemical and alpha-band neuro-physio-chemical alignments replicated in an independent dataset of individuals with asymptomatic amyloid-ß accumulation. DISCUSSION: Our findings demonstrate that AD pathology aligns topographically with the cortical distribution of chemical neuromodulator systems and scales with clinical severity, with implications for potential pharmacotherapeutic pathways. HIGHLIGHTS: Changes in cortical rhythms in Alzheimer's are organized along neurochemical boundaries. The strength of these alignments is related to clinical symptom severity. Deposition of amyloid-ß (Aß) is aligned with similar neurotransmitter systems. Aß deposition mediates the alignment of beta rhythms with cholinergic systems. Most alignments replicate in participants with pre-clinical Alzheimer's pathology.

Genetic Foundations of Neurophysiological and Behavioural Variability Across the Lifespan.

Neurophysiological brain activity underpins cognitive functions and behavioural traits. Here, we sought to establish to what extent individual neurophysiological traits spontaneously expressed in ongoing brain activity are primarily driven by genetic variation. We also investigated whether changes in such neurophysiological features observed across the lifespan are supported by longitudinal changes in cortical gene expression. We studied the heritability of neurophysiological traits from task-free brain activity of monozygotic and dizygotic twins as well as non-related individuals recorded with magnetoencephalography. We found that these traits were more similar between monozygotic twins compared to dizygotic twins, and that these heritable core dynamical properties of brain activity are predominantly influenced by genes involved in neurotransmission processes. These genes are expressed in the cortex along a topographical gradient aligned with the distribution of major cognitive functions and psychological processes. Our data also show that the impact of these genetic determinants on cognitive and psychological traits increases with age. These findings collectively highlight the persistent genetic influence across the lifespan on neurophysiological brain activity that supports individual cognitive and behavioural traits.

The neurophysiological brain-fingerprint of Parkinson's disease.

BACKGROUND: Research in healthy young adults shows that characteristic patterns of brain activity define individual "brain-fingerprints" that are unique to each person. However, variability in these brain-fingerprints increases in individuals with neurological conditions, challenging the clinical relevance and potential impact of the approach. Our study shows that brain-fingerprints derived from neurophysiological brain activity are associated with pathophysiological and clinical traits of individual patients with Parkinson's disease (PD). METHODS: We created brain-fingerprints from task-free brain activity recorded through magnetoencephalography in 79 PD patients and compared them with those from two independent samples of age-matched healthy controls (N = 424 total). We decomposed brain activity into arrhythmic and rhythmic components, defining distinct brain-fingerprints for each type from recording durations of up to 4 min and as short as 30 s. FINDINGS: The arrhythmic spectral components of cortical activity in patients with Parkinson's disease are more variable over short periods, challenging the definition of a reliable brain-fingerprint. However, by isolating the rhythmic components of cortical activity, we derived brain-fingerprints that distinguished between patients and healthy controls with about 90% accuracy. The most prominent cortical features of the resulting Parkinson's brain-fingerprint are mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also demonstrate that Parkinson's symptom laterality can be decoded directly from cortical neurophysiological activity. Furthermore, our study reveals that the cortical topography of the Parkinson's brain-fingerprint aligns with that of neurotransmitter systems affected by the disease's pathophysiology. INTERPRETATION: The increased moment-to-moment variability of arrhythmic brain-fingerprints challenges patient differentiation and explains previously published results. We outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and symptom laterality of Parkinson's disease. Thus, the proposed definition of a rhythmic brain-fingerprint of Parkinson's disease may contribute to novel, refined approaches to patient stratification. Symmetrically, we discuss how rhythmic brain-fingerprints may contribute to the improved identification and testing of therapeutic neurostimulation targets. FUNDING: Data collection and sharing for this project was provided by the Quebec Parkinson Network (QPN), the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer's Disease (PREVENT-AD; release 6.0) program, the Cambridge Centre for Aging Neuroscience (Cam-CAN), and the Open MEG Archives (OMEGA). The QPN is funded by a grant from Fonds de Recherche du Québec - Santé (FRQS). PREVENT-AD was launched in 2011 as a $13.5 million, 7-year public-private partnership using funds provided by McGill University, the FRQS, an unrestricted research grant from Pfizer Canada, the Levesque Foundation, the Douglas Hospital Research Centre and Foundation, the Government of Canada, and the Canada Fund for Innovation. The Brainstorm project is supported by funding to SB from the NIH (R01-EB026299-05). Further funding to SB for this study included a Discovery grant from the Natural Sciences and Engineering Research Council of Canada of Canada (436355-13), and the CIHR Canada research Chair in Neural Dynamics of Brain Systems (CRC-2017-00311).

Alignments between cortical neurochemical systems, proteinopathy and neurophysiological alterations along the Alzheimer's disease continuum.

Two neuropathological hallmarks of Alzheimer's disease (AD) are the accumulation of amyloid-ß (Aß) proteins and alterations in cortical neurophysiological signaling. Despite parallel research indicating disruption of multiple neurotransmitter systems in AD, it has been unclear whether these two phenomena are related to the neurochemical organization of the cortex. We leveraged task-free magnetoencephalography and positron emission tomography, with a cortical atlas of 19 neurotransmitters to study the alignment and interactions between alterations of neurophysiological signaling, Aß deposition, and the neurochemical gradients of the human cortex. In patients with amnestic mild cognitive impairment (N = 18) and probable AD (N = 20), we found that changes in rhythmic, but not arrhythmic, cortical neurophysiological signaling relative to healthy controls (N = 20) are topographically aligned with cholinergic, serotonergic, and dopaminergic neurochemical systems. These neuro-physio-chemical alignments are related to the severity of cognitive and behavioral impairments. We also found that cortical Aß plaques are preferentially deposited along neurochemical boundaries, and mediate how beta-band rhythmic cortical activity maps align with muscarinic acetylcholine receptors. Finally, we show in an independent dataset that many of these alignments manifest in the asymptomatic stages of cortical Aß accumulation (N = 33; N = 71 healthy controls), particularly the Aß-neurochemical alignments (57.1%) and neuro-physio-chemical alignments in the alpha frequency band (62.5%). Overall, the present study demonstrates that the expression of pathology in pre-clinical and clinical AD aligns topographically with the cortical distribution of chemical neuromodulator systems, scaling with clinical severity and with implications for potential pharmacotherapeutic pathways.

Brain mechanisms involved in the perception of emotional gait: A combined magnetoencephalography and virtual reality study.

Brain processes associated with emotion perception from biological motion have been largely investigated using point-light displays that are devoid of pictorial information and not representative of everyday life. In this study, we investigated the brain signals evoked when perceiving emotions arising from body movements of virtual pedestrians walking in a community environment. Magnetoencephalography was used to record brain activation in 21 healthy young adults discriminating the emotional gaits (neutral, angry, happy) of virtual male/female pedestrians. Event-related responses in the posterior superior temporal sulcus (pSTS), fusiform body area (FBA), extrastriate body area (EBA), amygdala (AMG), and lateral occipital cortex (Occ) were examined. Brain signals were characterized by an early positive peak (P1;∼200ms) and a late positive potential component (LPP) comprising of an early (400-600ms), middle (600-1000ms) and late phase (1000-1500ms). Generalized estimating equations revealed that P1 amplitude was unaffected by emotion and gender of pedestrians. LPP amplitude showed a significant emotion X phase interaction in all regions of interest, revealing i) an emotion-dependent modulation starting in pSTS and Occ, followed by AMG, FBA and EBA, and ii) generally enhanced responses for angry vs. other gait stimuli in the middle LPP phase. LPP also showed a gender X phase interaction in pSTS and Occ, as gender affected the time course of the response to emotional gait. Present findings show that brain activation within areas associated with biological motion, form, and emotion processing is modulated by emotional gait stimuli rendered by virtual simulations representative of everyday life.

Associations between neuromelanin depletion and cortical rhythmic activity in Parkinson's disease.

Background and Objectives: Parkinson's disease (PD) is marked by the death of neuromelanin-rich dopaminergic and noradrenergic cells in the substantia nigra (SN) and the locus coeruleus (LC), respectively, resulting in motor and cognitive impairments. While SN dopamine dysfunction has clear neurophysiological effects, the impact of reduced LC norepinephrine signaling on brain activity in PD remains to be established. Methods: We used neuromelanin-sensitive T1-weighted MRI (NPD = 58; NHC = 27) and task-free magnetoencephalography (NPD = 58; NHC = 65) to identify neuropathophysiological factors related to the degeneration of the LC and SN in patients with PD. Results: We found pathological increases in rhythmic alpha (8 - 12 Hz) activity in patients with decreased LC neuromelanin, with a stronger association in patients with worse attentional impairments. This negative alpha-LC neuromelanin relationship is also stronger in fronto-motor cortices, which are regions with high densities of norepinephrine transporters in the healthy brain, and where alpha activity is negatively related to attention scores. These observations support a noradrenergic association between LC integrity and alpha band activity. Our data also show that rhythmic beta (15 - 29 Hz) activity in the left somato-motor cortex decreases with lower levels of SN neuromelanin; the same regions where beta activity reflects axial motor symptoms. Discussion: Together, our findings clarify the association of well-documented alterations of rhythmic neurophysiology in PD with cortical and subcortical neurochemical systems. Specifically, attention-related alpha activity reflects dysfunction of the noradrenergic system, and beta activity with relevance to motor impairments reflects dopaminergic dysfunction.

Alterations of Cortical Structure and Neurophysiology in Parkinson's Disease Are Aligned with Neurochemical Systems.

OBJECTIVE: Parkinson's disease (PD) affects the structural integrity and neurophysiological signaling of the cortex. These alterations are related to the motor and cognitive symptoms of the disease. How these changes are related to the neurochemical systems of the cortex is unknown. METHODS: We used T1-weighted magnetic resonance imaging (MRI) and magnetoencephalography (MEG) to measure cortical thickness and task-free neurophysiological activity in patients with idiopathic PD (nMEG = 79, nMRI = 65) and matched healthy controls (nMEG = 65, nMRI = 37). Using linear mixed-effects models, we examined the topographical alignment of cortical structural and neurophysiological alterations in PD with cortical atlases of 19 neurotransmitter receptor and transporter densities. RESULTS: We found that neurophysiological alterations in PD occur primarily in brain regions rich in acetylcholinergic, serotonergic, and glutamatergic systems, with protective implications for cognitive and psychiatric symptoms. In contrast, cortical thinning occurs preferentially in regions rich in noradrenergic systems, and the strength of this alignment relates to motor deficits. INTERPRETATION: This study shows that the spatial organization of neurophysiological and structural alterations in PD is relevant for nonmotor and motor impairments. The data also advance the identification of the neurochemical systems implicated. The approach uses novel nested atlas modeling methodology that is transferrable to research in other neurological and neuropsychiatric diseases and syndromes. ANN NEUROL 2024;95:802-816.

Classification methods for ongoing EEG and MEG signals

Classification algorithms help predict the qualitative properties of a subject's mental state by extracting useful information from the highly multivariate non-invasive recordings of his brain activity. In particular, applying them to Magneto-encephalography (MEG) and electro-encephalography (EEG) is a challenging and promising task with prominent practical applications to e.g. Brain Computer Interface (BCI). In this paper, we first review the principles of the major classification techniques and discuss their application to MEG and EEG data classification. Next, we investigate the behavior of classification methods using real data recorded during a MEG visuomotor experiment. In particular, we study the influence of the classification algorithm, of the quantitative functional variables used in this classifier, and of the validation method. In addition, our findings suggest that by investigating the distribution of classifier coefficients, it is possible to infer knowledge and construct functional interpretations of the underlying neural mechanisms of the performed tasks. Finally, the promising results reported here (up to 97 percent classification accuracy on 1-second time windows) reflect the considerable potential of MEG for the continuous classification of mental states.