Introduction:The study investigated glioma patients after surgical resection of tumor tissue using postoperative functional magnetic resonance imaging (fMRI) to assess cavity-adjacent (perilesional) functional connectivity as a predictor of overall survival and functional recovery. Methods:We developed an analytic method to quantify the postoperative whole-brain functional connectivity. Resting-state whole-brain fMRI scans acquired from 12 glioma patients following surgical resection were analyzed as part of a proof-of-concept study. In particular, connectivity of the resected perilesional area was compared to that of the corresponding contralateral homologue region, and the difference between perilesional and contralateral connectivity was calculated. To test whether the functional connectivity metric could predict recovery of neurological outcomes, we compared patients' connectivity metrics from postoperative scans with changes in Karnofsky Performance Status (KPS) score between preoperative assessment and 6-month follow-up. Additionally, we examined whether the connectivity metric could predict overall survival by dividing the patients into subgroups based on their median survival time and comparing connectivity metrics. Results:Our analysis showed altered functional connectivity between perilesional and corresponding contralateral regions following surgical resection of glioma. The connectivity metric from postoperative scans was significantly correlated with recovery of neurological outcomes, as reflected by changes in KPS from preoperative to 6 months postoperative period (ρ = 0.97, p < 0.001). Moreover, individuals with survival times greater than 15 months showed significantly higher connectivity than those with shorter survival times (p = 0.0016 and Cohen's d = 2.74 in all subjects, p = 0.02 and Cohen's d = 1.90 in the subset of subjects with Grade IV gliomas). Furthermore, we developed machine learning models based on functional connectivity features, and they were able to predict the survival time with an accuracy of 92% and predict the KPS changes with an absolute error of 5.84 ± 6.08. Discussion:Overall, our study showed that resting-state fMRI from patients after glioma resection is relevant to their long-term neurological outcomes: decreased connectivity in the perilesional regions compared to the contralateral regions indicates less survival time and worsened functional outcomes. The reported analytics from postsurgical fMRI scans, combined with the machine learning model, could provide important prognostic information for postsurgical recovery management.

Transcranial magnetic stimulation (TMS) is widely used in both clinical and research settings to study and treat neurological and neuropsychiatric disorders, yet its underlying neural mechanisms remain unclear; particularly how stimulation influences both local and distant regional activity in relation to behavior. In this study, we combined single-pulse TMS with concurrent whole-head functional near-infrared spectroscopy (fNIRS) to examine hemodynamic and behavioral responses during a working memory task, with a focus on behavioral variability. Single TMS pulses were delivered to the left dorsolateral prefrontal cortex (DLPFC) while healthy participants rested; additionally, single pulses were delivered online to the left DLPFC while participants performed the working memory task. Across all participants, we observed a reliable load-dependent increase in hemodynamic activity associated with task. However, behavioral responses to TMS varied during the task. When participants were stratified into subgroups based on performance, a distinct topographic pattern emerged. During the task, TMS systematically modulated hemodynamic responses in regions including DLPFC, superior medial gyrus, precuneus, and parietal lobule, which are areas belonging to the default mode network. Moreover, the hemodynamic response during the single pulse alone sessions without any task was also found to be associated with the behavioral responses in a coherent pattern involving DLPFC, precuneus and parietal lobules. These findings suggest that variable behavioral outcomes during online TMS task are linked to distinct hemodynamic responses in a topographic pattern of local and distant regional areas.

Histone modifications have emerged as critical epigenetic regulators in the development and progression of mental disorders. This review synthesizes recent advances in the field, highlighting both canonical modifications, such as methylation, acetylation, and phosphorylation, as well as novel discoveries that link neurotransmitter signaling to chromatin regulation. In particular, neurotransmitter-mediated histone modifications, including serotonylation, dopaminylation, and histaminylation, represent a compelling new paradigm by which neuronal activity and environmental stimuli can induce lasting changes in gene expression. Aberrant histone modifications have been implicated in the risk, symptomatology, and treatment response of psychiatric conditions such as depression, bipolar disorder, and schizophrenia. Furthermore, therapeutic strategies that target histone-modifying enzymes, most notably histone deacetylase inhibitors, are being actively explored for their potential to restore epigenetic balance and improve clinical outcomes. A deeper understanding of the mechanistic diversity and disease specificity of histone modifications will be crucial for the development of precision epigenetic therapies in psychiatry.

Abstract Background Bipolar disorder (BIP) and obsessive-compulsive disorder (OCD) frequently co-occur and show evidence of genetic overlap, yet the specific pleiotropic loci and their functional mechanisms remain unclear. Methods We conducted large-scale genetic analyses using GWAS summary statistics for BIP and OCD, excluding 23andMe data. We applied conjunctional FDR analysis to identify pleiotropic variants jointly associated with BIP and OCD, followed by integrative annotation through transcriptomic (eQTL, sQTL), epigenomic (mQTL, haQTL), and proteomic (pQTL, histone PTM) data. SMR analysis was used to prioritize putative regulatory effects, while AlphaGenome predictions and targeted histone proteomics were employed to evaluate allele-specific chromatin changes. Results We observed a significant genetic correlation ( r g = 0.38, P = 3.8 × 10 -29 ) and extensive polygenic overlap between BIP and OCD. Bidirectional MR supported causal effects in both directions, with stronger evidence for BIP influencing OCD risk. ConjFDR analysis revealed 2,143 pleiotropic SNPs jointly associated with BIP and OCD, with convergent signals at the ITIH3/ITIH4 locus. Summary-data-based Mendelian randomization (SMR) and colocalization with multi-omic QTLs (eQTL, pQTL, mQTL, and haQTL) further prioritized the ITIH3/4 locus, where multiple SNPs (e.g., rs3774364) colocalized with H3K27ac histone acetylation QTLs in the prefrontal cortex (PP_H4 > 0.5). Integrated PBMC RNA-seq and complementary histone mass spectrometry linked immune–ECM transcriptional activity to exploratory global histone acetylation changes in BIP and OCS-BIP, with suggestive alterations in H3K27ac-containing peptides. Conclusions Our multi-omic analysis highlights ITIH3/ITIH4 as a prioritized pleiotropic locus for BIP and OCD. Epigenetic regulation, particularly through histone acetylation, may underlie shared susceptibility and offers a novel mechanistic link between these psychiatric disorders.

Chemotherapy is a common treatment for women with ovarian cancer, and many women treated with chemotherapy experience cognitive dysfunction. However, few studies have investigated the mechanisms of chemotherapy-related cognitive impairment in women with ovarian cancer. The goal of this study was to assess the relationships among neurovascular coupling, iron levels and cognitive functions in women with ovarian cancer after first-line chemotherapy. Simultaneous fNIRS and EEG data were collected in women diagnosed with advanced stage ovarian cancer at baseline (N = 13) and after 3-9 rounds of chemotherapy (N = 8). The attentional network task (ANT) was administered for neurocognitive evaluation. Blood iron levels were measured using standard clinical assays. In parallel, similar concurrent fNIRS-EEG data were acquired in a group of 10 healthy participants in a test-retest design. Cognitive performance declined, as indicated by decreases in ANT sub scores after chemotherapy. Blood iron biomarkers indexing oxygen transport also declined and were related to decreases in the ANT sub scores. Both fNIRS and EEG data responses were shown to have excellent reliability in the healthy subjects, while cancer patients showed significant decreases in oxygenated hemoglobin response in fNIRS, despite no changes in EEG responses after chemotherapy. A significant dose relationship was also found in the changes of fNIRS responses. Our data indicate that chemotherapy produced cognitive deficits and decreases in the oxygenated hemoglobin response and that these may be related to reduced oxygen transport capacity. Results also suggest the utility of using fNIRS-EEG to monitor the progression of cognitive impairment and characterize those mechanisms.

It has long been established that human brains remain functionally active at rest, as demonstrated with the discovery of resting-state networks (RSNs) underlying spontaneous neural activity. Recent studies suggest that classical RSNs estimated from functional magnetic resonance imaging (fMRI) data using time-domain functional connectivity measures might be driven by recurring point-process events. Due to the slow hemodynamic response, fMRI cannot reveal such point-processes at the timescale of neuronal events while electroencephalography (EEG) holds the promise due to its millisecond temporal resolution and successful reconstruction of fMRI-like RSNs. The present study reported a set of recurring transient (<100 ms) cortical co-activation patterns (CAPs) derived from resting-state EEG using a clustering algorithm with spatial-domain measures (i.e., k-means). Our results indicate that this set of CAPs exhibit strong spatial correspondence with known RSNs, not only those derived from the same EEG data using time-domain measures (i.e., independence), but also those from fMRI literature, covering visual, auditory, motor, limbic, high-order, and default mode networks. CAPs exhibit the properties of hemispheric symmetry, spatially separatable sub-systems, and intersubject variability gradient across functional systems, which have all been observed in classical RSNs. These findings suggest that classical RSNs might be driven by recurring transient neuronal activations captured in CAPs. More importantly, CAPs can reveal the fast dynamics of such brain-wide networked neuronal activations (e.g., different CAPs exhibit significantly different occurrences and lifetimes) and benefit from their intersubject reproducibility, thus underscoring their potential to advance our understanding on neuronal mechanisms of spontaneous large-scale brain activation phenomena.

Background: Major depressive disorder (MDD) encompasses a broad spectrum of heterogeneous symptoms arising from distinct etiological mechanisms. Phenotypic markers of psychopathology are most likely influenced by exposure to childhood maltreatment, yielding distinct subtypes within conventional diagnostic boundaries. However, the biological interactions between MDD subtypes and types of childhood trauma remain unclear. Methods: 50 atypical depression (AD) patients, 97 non-AD patients and 50 healthy controls were included to complete multi-shell diffusion MRI scans and clinical assessments. Differential tractography was performed to clarify the axonal injury between the AD and non-AD groups. Moreover, correlational tractography was employed to individually assess the relationship between quantitative anisotropy (QA) and all types of childhood trauma in each depressed subgroup. Results: Our study found that AD and non-AD patients had differential axonal loss primarily involving the bilateral superior longitudinal fasciculus, arcuate fasciculus, inferior longitudinal fasciculus, parietal aslant tract, and corpus callosum. Furthermore, AD patients showed significantly negative associations between QA values, childhood trauma total scores, and threat-related adversity, while significantly positive associations were observed in non-AD patients. However, similar phenomena were not observed for deprivation-related adversities. Discussion: Our findings indicate differential spatial patterns of axonal alterations associated with threat-related adversity in atypical depression and non-atypical depression. Efforts to attenuate the consequences of childhood maltreatment for MDD should consider the associations between specific patterns of adversity and specific clinical manifestations.

BACKGROUND:The heterogeneity of symptoms in major depressive disorder is impeding progress toward patient-specific treatment strategies and course trajectories. Origins of such differential clinical manifestations likely have dissociable pathophysiologies, but neural substrates associated with specific atypical depressive symptoms remain elusive. METHODS:The muti-shell diffusion MRI images were acquired from 50 patients with atypical depression (AD), 97 patients with non-atypical depression (non-AD), and 50 healthy controls (HCs). We used gray matter-specific multi-compartment diffusion models (cortical-neurite orientation dispersion and density imaging and free-water elimination model) to assess abnormalities of gray matter microstructure associated with AD. Superficial U-fibers analysis was performed to clarify short-range cortico-cortical connections. RESULTS:Abnormalities in intracellular volume fraction (ICVF) and free-water fraction anisotropy were found in the superior frontal gyrus, middle frontal gyrus, inferior parietal gyrus, and superior parietal gyrus across three groups. Post-hoc pairwise comparative analysis yielded similar results. While adjusting for the effects of age, gender, education, and the ICVF mentioned above, AD patients showed significantly higher scores in reversed neurovegetative symptoms and leaden paralysis compared with non-AD patients. Moreover, diagnosis-related alterations in ICVF of right caudal middle frontal gyrus and education-related changes in ICVF of right superior frontal gyrus were significantly associated with hypersomnia. We also found that underlying superficial U-fibers reflected deficits in cortical-derived neurite density. CONCLUSIONS:Cortical-derived neurite density abnormalities were significantly associated with atypical depressive symptoms, capturing interindividual etiological heterogeneity in patients with major depressive disorder. Cortical-derived neurite density within the medial prefrontal gyrus may be a robust biomarker for atypical depressive symptoms of AD.

BACKGROUND:The study aimed to explore the risk factors of suicidal attempts (SA) in patients with major depressive disorders (MDD). METHODS:Cross-sectional analysis of 3247 MDD patients from the National Survey on Symptomatology of Depression (NSSD) was conducted, with data split 7:3 for training/validation. Boruta's Algorithm and Lasso screened predictors, while logistic regression and nomogram were used to identify independent risk factors of SA and visualize the overall impact of these factors on the SA risk of each patient. RESULTS:392 (12.1 %) patients were found to have a history of SA. Boruta's Algorithm and Lasso analysis identified 20 variables as significant risk factors of SA, especially self-harm and a sense of decreased ability. Logistic regression analysis found that No. of hospitalization (OR = 1.14, 95 %CI:1.06, 1.23), SSRIs use(OR = 1.57,95 %CI:1.03,2.38), antipsychotics use (OR = 1.82,95 %CI:1.08,3.04), mood congruent psychosis (OR = 1.32,95 %CI:1.03,1.70), self-harm (OR = 70.90,95 %CI:45.70, 113.00), gastrointestinal system complaints (OR = 1.42,95 %CI:1.13, 1.78), weight gain (OR = 2.11,95 %CI:1.04, 4.29),the feelings of helplessness(OR = 1.47,95 %CI:1.11,1.95), unhappiness (OR = 1.52,95 %CI:1.14, 2.00) and derealization (OR = 1.33,95 %CI:1.02, 1.72) were independently and significantly associated with increased risk of SA in MDD patients. The prediction model showed robust performance, with an area under the curves (AUC) of the receiver-operator characteristics of 0.935 and 0.937 in the training set and validation set, respectively. CONCLUSION:The findings may help develop assessment tools for suicide risk in MDD patients and provide clues for further mechanistic studies. LIMITATIONS:Because of the study's cross-sectional design, causality could not be established between the predictors and SA, and the retrospective data collection approach may introduce recall bias.

Functional near-infrared spectroscopy (fNIRS) is an optical imaging modality which, similar to fMRI, measures cerebral hemodynamics associated with neural activity. It has several advantages over fMRI, including low cost, portability, compatibility with metal or electrical medical implants, and ease of integration with electroencephalography (EEG) and transcranial magnetic stimulation (TMS). However, fNIRS signal contains a number of confounding components. Physiological noises due to superficial absorption by the scalp and skull are present in all fNIRS data. Additionally, low-frequency oscillations of respiration, cardiac pulse and movements all obscure the underlying cerebral hemodynamic signals. Our previous work has developed an automatic processing pipeline that effectively removes these physiological noise components from data during voluntary tasks (e.g., a motor task) and an endogenous state (e.g., awake resting) [1], [2]. However, to date it has not been known if the noises behave similarly in recordings involving an externally injected stimulus such as TMS. Therefore, in a unique setup of concurrent fNIRS, EEG and TMS (fNET), this study examined the spatial and temporal profiles of fNIRS data and noises during motor, single pulse and repetitive TMS. Specifically, we compared the multichannel short separation recordings with the regularly distanced long separation data in a whole-head montage. The results showed that superficial fluctuations indeed were present in the TMS-concurrent fNIRS recordings and that the noise components behaved similarly across motor task, single pulse and repetitive TMS at individuals’ alpha frequency, which warrants removal of such physiological noises.Clinical Relevance— Compared to fMRI, fNIRS offers a much less expensive alternative for measuring cortical hemodynamics. Importantly, fNIRS can provide a clinic accessible options for concurrent measurement with TMS when TMS is given as treatment to patients with depression or other neurological disorders. Our findings indicate that fNIRS data acquired during concurrent TMS are contaminated by superficial fluctuations and that careful removal of these physiological noises from fNIRS data is critical in obtaining accurate images of cerebral activity with fNIRS.

Introduction Neurologic impairment is common in patients with acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. While patients with severe COVID have a higher prevalence of neurologic symptoms, as many as one in five patients with mild COVID may also be affected, exhibiting impaired memory as well as other cognitive dysfunctions.Methods To characterize the effect of COVID on the brain, the current study recruited a group of adults with post-COVID cognitive complaints but with mild, non-hospitalized cases. They were then evaluated through formal neuropsychological testing and underwent functional MRI of the brain. The participants in our study performed nearly as expected for cognitively intact individuals. Additionally, we characterized the functional connectivity of the default mode network (DMN), which is known for cognitive functions including memory as well as the attention functions involved in normal aging and degenerative diseases.Results Along with the retention of functional connectivity in the DMN, our results found the DMN to be associated with neurocognitive performance through region-of-interest and whole-brain analyses. The connectivity between key nodes of the DMN was positively correlated with cognitive scores (r = 0.51, p = 0.02), with higher performers exhibiting higher DMN connectivity.Discussion Our findings provide neuroimaging evidence of the functional connectivity of brain networks among individuals experiencing cognitive deficits beyond the recovery of mild COVID. These imaging outcomes indicate expected functional trends in the brain, furthering understanding and guidance of the DMN and neurocognitive deficits in patients recovering from COVID.

Food addiction is associated with attention bias and response inhibition deficits, while the relationship between these two domains is unclear. Participants with body mass index (BMI) ≥ 25 and exhibiting food addiction behaviors (FA group, n = 20) were compared with healthy controls (HC group, n = 23). We examined attention-inhibition mechanisms using resting EEG microstate analysis, food-cue-evoked event-related potentials (ERPs), and non-food Go/No-Go tasks. Overweight individuals with food addiction behaviors demonstrated attentional deficits, as indicated by abnormalities in microstate D and the P100 component. Importantly, both microstate D and the P100 component significantly predicted No-Go performance, linking neurophysiological markers to behavioral inhibition. This study suggests that attention bias may be an important interaction factor of response inhibition, providing novel mechanistic insights into food addiction.

Degenerative cervical myelopathy (DCM) is an age-related, non-traumatic disease that results from compression of the cervical spinal cord, leading to gray matter (GM) loss and impaired spinal reflex arcs. We utilized a stimulation paradigm to investigate the impact that compressive damage has on GM pathways. Involuntary motor thresholds were determined using electrical stimulation to the median nerve of the dominant arm in 28 DCM patients and 19 healthy controls (HCs). To evaluate structural-functional impairments in DCM, we measured involuntary motor thresholds alongside MRI-derived GM volume and WM magnetization transfer ratio (MTR) measures using automated segmentation at the level of maximum compression level (MCL). Results showed that DCM patients required a higher motor threshold than HCs to evoke an involuntary motor response ($\mathbf{p}<0.05$). These patients had significant GM volume decreases in the overall ($\mathrm{p}<0.001$), dorsal ($\mathrm{p}<0.001$), intermediate ($\mathrm{p}<0.001$), and ventral ($\mathrm{p}<0.001$) regions. Ventral horn GM volume also correlated with ventral region MTR values in DCM patients ($\mathrm{r}=0.49; \mathrm{p}<0.05$). Higher motor threshold in DCM may reflect potential nerve damage caused by myelopathy progression. However, the lack of correlation between GM or WM measures and motor threshold was unexpected and may indicate alternative sources of neurological impediment outside of the spinal cord.

Functional spinal cord MRI (fMRI) is an emerging technique for evaluating sensorimotor responses in both healthy and disease states. However, degenerative cervical myelopathy (DCM), a non-traumatic, age-related pathology characterized by spinal cord compression, often leads to motor and sensory impairments that make standard task-based fMRI paradigms challenging to perform or execute. To overcome this, we implemented an fMRI paradigm using direct electrical stimulation to the median nerve to evoke neural responses in both motor and sub-motor spinal cord pathways. In this study, 23 DCM and 23 aged-matched healthy controls (HC) underwent two task-based spinal cord fMRI sessions with direct, median nerve stimulation to the right upper limb. Motor thresholds were determined on an individual basis by a thumb/finger twitching, and sub-motor thresholds were set to be 15% underneath motor with no visible twitching. Blood Oxygenation Level Depended (BOLD) signals were analyzed across the C5-C8 cervical spinal cord segments. HCs demonstrated increased activation during motor vs sub-motor stimulation (1155 vs 496 voxels; +7.1%), whereas DCM patients showed reduction in activation with higher stimulation (1028 vs 1220 voxels; -2.2%). This activation pattern was consistent across the cervical spinal segments, with HCs peaking at C7 (sub-motor: 328 voxels, motor: 608 voxels), which is consistent with median nerve input for forearm and hand. While DCM responses were altered, with sub-motor differentially maximized at C6 (406 voxels) and abnormal recruitment at C5 (112 voxels) and C8 (198 voxels). Spatially, HC responses were focused in the gray matter (GM) and bilateral, whereas DCM responses showed reduced GM localization and were more ipsilateral. Furthermore, we saw spikes in sub-motor DCM deactivation at the overall (3032 voxels) and C6 root level (1500 voxels) that were greater than the other conditions. This study demonstrates the feasibility and effectiveness of median nerve stimulation as a task-based paradigm for assessing spinal cord function in those with spinal cord compression. Group level differences between DCM patients and age-matched HCs showed spatial and quantitatively distinct patterns across stimulation thresholds and spinal segments. Our findings suggest disrupted sub-motor and motor activation in the DCM group, highlighting the use of this approach as a functional biomarker to capture disease specific alterations.

Brain tumors significantly disrupt brain network organization, yet the temporal dynamics of network reorganization following surgical intervention remain poorly understood. This study investigated longitudinal changes in functional brain network properties across pre-surgical, post-surgical, and follow-up time points in glioma patients. Using graph theory analysis of resting-state functional magnetic resonance imaging (fMRI) data, we examined whole-brain network metrics as well as the connections involving perilesional and contralesional regions. Results revealed significant alterations in network topology over time, with distinct patterns of reorganization in perilesional and contralesional regions, suggesting mechanisms of plasticity and recovery in brain network architecture following tumor resection.Clinical Relevance—These findings have significant implications for surgical planning and post-operative care, suggesting the need for therapeutic approaches that consider both local and distant network effects. The demonstrated importance of contralesional adaptation particularly warrants attention in rehabilitation strategies, potentially opening new avenues for targeted interventions in recovery.

Large-scale distributed activations in various modes of spatiotemporal organizations have been extensively reported in both hemodynamic and electrical/magnetic human brain signals, which provides knowledge on how information is being hierarchically processed and integrated among functionally linked brain regions. These large-scale distributed activations have also been identified in brain signals from animals, indicating that they are preserved brain organizations in species evolution. Recent studies using human electroencephalography (EEG) and magnetoencephalography (MEG) have further revealed that large-scale distributed activations are frequency-specific and of fast dynamics (tens of milliseconds), while these phenomena have not been investigated in animals. The present study used electrocorticography (ECoG) data recorded with the coverage of nearly entire hemisphere(s) to investigate the existence of time-resolved large-scale coactivation patterns (CAPs) in monkey brains and compare them to CAPs from whole-head human EEG data both at resting states. The present results reveal brain-wide patterns of CAPs in monkey ECoG data, which share significant similarities to human EEG CAPs, both in the alpha band, on spatial and temporal patterns not only in individual CAPs but also on relative differences among different CAPs. The transition patterns among all monkey ECoG CAPs further reveal a similar superstructure as in human EEG CAPs that controls the dynamics of brain state transitions at rest and their spatial expressions. These findings suggest that large-scale brain events of fast dynamics exist in non-human primates and they are of functional importance cross species, similar as time-averaged ones that have been well reported in literature.

Analysis of co-activation patterns (CAPs) has become a powerful means to capture time-varying brain activations over classical time-averaged functional connectivity measures. Events represented in CAPs are transient quasi-stable states of brain-wide activation, which allow the modeling of large-scale neuronal activations through the sequence/speed of their transitions and the spatial patterns they express. CAPs have been applied in multiple neuroimaging modalities and in different population groups, in which they have demonstrated consistent temporal and spatial patterns. In the present study, CAP analysis was applied to a large electroencephalography (EEG) dataset ($n=449$) from infants ($n=47$) in a longitudinal study within their first year of life ($\mathbf{4}-\mathbf{1 0}$ months). Our results demonstrate that CAPs can be reliably constructed in infants, similar to adult CAPs in previous literature, and consistently identified at their different stages of maturation. Moreover, statistically significant correlations in CAP lifetimes, occurrences, and spatial patterns with respect to infant age were observed. These results indicate both the sensitivity and robustness of CAPs to infant age, which might provide valuable insights on early functional development in brain networks beyond early structural connectivity changes [1], and their associations with skill acquisition and behavioral development.

Various neuroimaging techniques and data analytics have been established in studying brain-wide activations in human. However, a significant impediment in studying brain-wide activations have been limited in its temporal resolutions, due to either slow response times in the signal of interest or amount of data required by the special analytics used. To overcome these limitations, we have developed modeling and computational tools for noninvasive electrical imaging of fast brain-wide neuronal activations in human brains from high-density electroencephalography (EEG). Here we report a set of brain-wide co-activation patterns (bwCAPs) at the timescales of tens of milliseconds from dominant resting rhythmic human EEG at the alpha band, which indicate multiple recurring transient brain states and a well-organized transitional structure among them. This is the first time in detecting brain-wide modulating patterns on the well-established neuronal oscillation. Due to the distributed nature of these bwCAPs at the brain-wide scale, their timescales close to the actual neuronal events, and frequency-specific detections, we expect that these phenomena will support future investigations on human cognition noninvasively.

Degenerative cervical myelopathy (DCM) is a progressive non-traumatic spine condition predominantly affecting individuals above the age of 50 years. It is associated with compression of the cervical spinal cord (SC), which can occur at one or multiple vertebral levels. However, SC tissue injury, particularly at the maximum level of compression (MCL), is typically a focal point of interest. Currently, there is no standardized approach for MCL localization, with researchers often relying on the visual inspection of magnetic resonance imaging (MRI) scans. Such inspection is often performed manually, which is time-consuming and can introduce bias. The present study explores the applicability of SC morphometric measures for precise MCL localization in DCM patients. We retrospectively analyzed 15 DCM patients (mean age: 57.8 years; 3 males) scanned on a GE MRI scanner adhering to the generic spine protocol. The T2-weighted structural data were processed semi-automatically using the spinal cord toolbox. SC morphometric measures including the anterior-posterior (AP) diameter, maximum SC compression (MSCC), and compression ratio (CR) were calculated. Our results show that AP diameter decreased, MSCC increased, and CR decreased at the site of compression. Furthermore, the maximum and minimum of MSCC and CR plots against the cervical SC length, respectively can be used to quantitatively locate the MCL in DCM patients. Such automation in the process of localizing MCL in DCM studies can help reduce time and minimize operator bias. Future work involves increasing sample size to further validate current findings.