We present an approach for tracking fast spatiotemporal cortical dynamics in which we combine white matter connectivity data with source-projected electroencephalographic (EEG) data. We employ the mathematical framework of graph signal processing in order to derive the Fourier modes of the brain structural connectivity graph, or "network harmonics". These network harmonics are naturally ordered by smoothness. Smoothness in this context can be understood as the amount of variation along the cortex, leading to a multi-scale representation of brain connectivity. We demonstrate that network harmonics provide a sparse representation of the EEG signal, where, at certain times, the smoothest 15 network harmonics capture 90% of the signal power. This suggests that network harmonics are functionally meaningful, which we demonstrate by using them as a basis for the functional EEG data recorded from a face detection task. There, only 13 network harmonics are sufficient to track the large-scale cortical activity during the processing of the stimuli with a 50 ​ms resolution, reproducing well-known activity in the fusiform face area as well as revealing co-activation patterns in somatosensory/motor and frontal cortices that an unconstrained ROI-by-ROI analysis fails to capture. The proposed approach is simple and fast, provides a means of integration of multimodal datasets, and is tied to a theoretical framework in mathematics and physics. Thus, network harmonics point towards promising research directions both theoretically - for example in exploring the relationship between structure and function in the brain - and practically - for example for network tracking in different tasks and groups of individuals, such as patients.

Background: Adolescent idiopathic scoliosis (AIS) is a spinal deformity that affects approximately 4% of the world’s population. Several hypotheses regarding the etiology of AIS have been investigated. In the last decades, impaired visual-spatial perception, alterations in spatial body orientation and sensory integration deficits have been documented. Objective: We aimed to summarize the neurophysiological, balance, and motion evidence related to AIS published in the last fifteen years, between January 2008 and April 2023. Both observational and interventional studies were considered. Only studies using quantitative assessment methods, such as electroencephalography (EEG), electromyography (EMG), magnetic resonance imaging (MRI), somatosensory evoked potentials, force platform, or motion capture, were included. Methods: 1250 eligible records identified from online database searching were filtered by duplicate removal, title and abstract screening, and qualitative analysis. 61 articles met the inclusion criteria (i.e., Cobb range 10°-35°, age range 10-18 years) and were summarized. Results: We found significant evidence of impaired standing balance in individuals with AIS who greatly rely on visual and proprioceptive information to stay upright. EMG studies frequently reported an increased activity on the convex side of the intrinsic spinae muscles. EEG data show increased delta and theta power, higher alpha peak frequencies, and significant suppression in the alpha and beta bands in subjects with AIS during standing tasks. MRI studies report changes in white matter structures, differences in the vestibular system, and abnormal cortical activations over motor-related areas in subjects with AIS. Bracing appears to be an effective treatment for AIS, leading to improvements in static balance and gait. Methodological issues prevent reliable conclusions about the effects of other treatment options. Conclusions: This review underscores the importance of quantitative assessment methods to explore the etiology and pathophysiology of AIS. Further research is needed to measure the impact of physical therapy and orthotic treatments on the neurophysiological mechanisms of the disease.

The cortico-striatal-pallidal-thalamic and limbic circuits are suggested to play a crucial role in the pathophysiology of depression. Stimulation of deep brain targets might improve symptoms in treatment-resistant depression. However, a better understanding of connectivity properties of deep brain structures potentially implicated in deep brain stimulation (DBS) treatment is needed. Using high-density EEG, we explored the directed functional connectivity at rest in 25 healthy subjects and 26 patients with moderate to severe depression within the bipolar affective disorder, depressive episode, and recurrent depressive disorder. We computed the Partial Directed Coherence on the source EEG signals focusing on the amygdala, anterior cingulate, putamen, pallidum, caudate, and thalamus. The global efficiency for the whole brain and the local efficiency, clustering coefficient, outflow, and strength for the selected structures were calculated. In the right amygdala, all the network metrics were significantly higher (p < 0.001) in patients than in controls. The global efficiency was significantly higher (p < 0.05) in patients than in controls, showed no correlation with status of depression, but decreased with increasing medication intake ( $${{\bf{R}}}^{{\bf{2}}}{\boldsymbol{=}}{\bf{0.59}}\,{\bf{and}}\,{\bf{p}}{\boldsymbol{=}}{\bf{1.52}}{\bf{e}}{\boldsymbol{ \mbox{-} }}{\bf{05}}$$R2=0.59andp=1.52e‐05 ). The amygdala seems to play an important role in neurobiology of depression. Practical treatment studies would be necessary to assess the amygdala as a potential future DBS target for treating depression.

Epilepsy diagnosis can be difficult in the absence of interictal epileptic discharges (IED) on scalp EEG. We used high-density EEG to measure connectivity in large-scale functional networks of patients with focal epilepsy (Temporal and Extratemporal Lobe Epilepsy, TLE and ETLE) and tested for network alterations during resting wakefulness without IEDs, compared to healthy controls. We measured global efficiency as a marker of integration within networks. We analysed 49 adult patients with focal epilepsy and 16 healthy subjects who underwent high-density-EEG and structural MRI. We estimated cortical activity using electric source analysis in 82 atlas-based cortical regions based on the individual MRI. We applied directed connectivity analysis (Partial Directed Coherence) on these sources and performed graph analysis: we computed the Global Efficiency on the whole brain and on each resting state network. We tested these features in different group of patients. Compared to controls, efficiency was increased in both TLE and ETLE (p < 0.05). The somato-motor-network, the ventral-attention-network and the default-mode-network had a significantly increased efficiency (p < 0.05) in both TLE and ETLE as well as TLE with hippocampal sclerosis. During interictal scalp EEG epochs without IED, patients with focal epilepsy show brain functional connectivity alterations in the whole brain and in specific resting-state-networks. This higher integration reflects a chronic effect of pathological activity within these structures and complement previous work on altered information outflow. These findings could increase the diagnostic sensitivity of scalp EEG to identify epileptic activity in the absence of IED.