Presurgical planning prior to brain tumor resection is critical for the preservation of neurologic function post-operatively. Neurosurgeons increasingly use advanced brain mapping techniques pre- and intra-operatively to delineate brain regions which are \"eloquent\" and should be spared during resection. Functional MRI (fMRI) has emerged as a commonly used non-invasive modality for individual patient mapping of critical cortical regions such as motor, language, and visual cortices. To map motor function, patients are scanned using fMRI while they perform various motor tasks to identify brain networks critical for motor performance, but it may be difficult for some patients to perform tasks in the scanner due to pre-existing deficits. Connectome fingerprinting (CF) is a machine-learning approach that learns associations between resting-state functional networks of a brain region and the activations in the region for specific tasks; once a CF model is constructed, individualized predictions of task activation can be generated from resting-state data. Here we utilized CF to train models on high-quality data from 208 subjects in the Human Connectome Project (HCP) and used this to predict task activations in our cohort of healthy control subjects (n = 15) and presurgical patients (n = 16) using resting-state fMRI (rs-fMRI) data. The prediction quality was validated with task fMRI data in the healthy controls and patients. We found that the task predictions for motor areas are on par with actual task activations in most healthy subjects (model accuracy around 90%-100% of task stability) and some patients suggesting the CF models can be reliably substituted where task data is either not possible to collect or hard for subjects to perform. We were also able to make robust predictions in cases in which there were no task-related activations elicited. The findings demonstrate the utility of the CF approach for predicting activations in out-of-sample subjects, across sites and scanners, and in patient populations. This work supports the feasibility of the application of CF models to presurgical planning, while also revealing challenges to be addressed in future developments. PRACTITIONER POINTS: Precision motor network prediction using connectome fingerprinting. Carefully trained models\' performance limited by stability of task-fMRI data. Successful cross-scanner predictions and motor network mapping in patients with tumor.

Objective.Although motor imagery-based brain-computer interface (MI-BCI) holds significant potential, its practical application faces challenges such as BCI-illiteracy. To mitigate this issue, researchers have attempted to predict BCI-illiteracy by using the resting state, as this was found to be associated with BCI performance. As connectivity\'s significance in neuroscience has grown, BCI researchers have applied connectivity to it. However, the issues of connectivity have not been considered fully. First, although various connectivity metrics exist, only some have been used to predict BCI-illiteracy. This is problematic because each metric has a distinct hypothesis and perspective to estimate connectivity, resulting in different outcomes according to the metric. Second, the frequency range affects the connectivity estimation. In addition, it is still unknown whether each metric has its own optimal frequency range. Third, the way that estimating connectivity may vary depending upon the dataset has not been investigated. Meanwhile, we still do not know a great deal about how the resting state electroencephalography (EEG) network differs between BCI-literacy and -illiteracy.Approach.To address the issues above, we analyzed three large public EEG datasets using three functional connectivity and three effective connectivity metrics by employing diverse graph theory measures. Our analysis revealed that the appropriate frequency range to predict BCI-illiteracy varies depending upon the metric. The alpha range was found to be suitable for the metrics of the frequency domain, while alpha + theta were found to be appropriate for multivariate Granger causality. The difference in network efficiency between BCI-literate and -illiterate groups was constant regardless of the metrics and datasets used. Although we observed that BCI-literacy had stronger connectivity, no other significant constructional differences were found.Significance.Based upon our findings, we predicted MI-BCI performance for the entire dataset. We discovered that combining several graph features could improve the prediction\'s accuracy.

Life-course exposure to risk and protective factors impacts brain macro- and micro-structure, which in turn affects cognition. The concept of brain-age gap assesses brain health by comparing an individual\'s neuroimaging-based predicted age with their calendar age. A higher BAG implies accelerated brain ageing and is expected to be associated with worse cognition. In this study, we comprehensively modelled mutual associations between brain health and lifestyle factors, brain age and cognition in a large, middle-aged population. For this study, cognitive test scores, lifestyle and 3T MRI data for n = 4881 participants [mean age (± SD) = 59.2 (±8.6), 50.1% male] were available from The Maastricht Study, a population-based cohort study with extensive phenotyping. Whole-brain volumes (grey matter, cerebrospinal fluid and white matter hyperintensity), cerebral microbleeds and structural white matter connectivity were calculated. Lifestyle factors were combined into an adapted LIfestyle for BRAin health weighted sum score, with higher score indicating greater dementia risk. Cognition was calculated by averaging z-scores across three cognitive domains (memory, information processing speed and executive function and attention). Brain-age gap was calculated by comparing calendar age to predictions from a neuroimaging-based multivariable regression model. Paths between LIfestyle for BRAin health tertiles, brain-age gap and cognitive function were tested using linear regression and structural equation modelling, adjusting for sociodemographic and clinical confounders. The results show that cerebrospinal fluid, grey matter, white matter hyperintensity and cerebral microbleeds best predicted brain-age gap (R 2 = 0.455, root mean squared error = 6.44). In regression analysis, higher LIfestyle for BRAin health scores (greater dementia risk) were associated with higher brain-age gap (standardized regression coefficient β = 0.126, P < 0.001) and worse cognition (β = -0.046, P = 0.013), while higher brain-age gap was associated with worse cognition (β=-0.163, P < 0.001). In mediation analysis, 24.7% of the total difference in cognition between the highest and lowest LIfestyle for BRAin health tertile was mediated by brain-age gap (β indirect = -0.049, P < 0.001; β total = -0.198, P < 0.001) and an additional 3.8% was mediated via connectivity (β indirect = -0.006, P < 0.001; β total = -0.150, P < 0.001). Findings suggest that associations between health- and lifestyle-based risk/protective factors (LIfestyle for BRAin health) and cognition can be partially explained by structural brain health markers (brain-age gap) and white matter connectivity markers. Lifestyle interventions targeted at high-risk individuals in mid-to-late life may be effective in promoting and preserving cognitive function in the general public.

Numerous studies reported inconsistent results concerning gender influences on the functional organization of the brain for language in children and adults. However, data for the gender differences in the functional language networks at birth are sparse. Therefore, we investigated gender differences in resting-state functional connectivity in the language-related brain regions in newborns using functional near-infrared spectroscopy. The results revealed that female newborns demonstrated significantly stronger functional connectivities between the superior temporal gyri and middle temporal gyri, the superior temporal gyri and the Broca\'s area in the right hemisphere, as well as between the right superior temporal gyri and left Broca\'s area. Nevertheless, statistical analysis failed to reveal functional lateralization of the language-related brain areas in resting state in both groups. Together, these results suggest that the onset of language system might start earlier in females, because stronger functional connectivities in the right brain in female neonates were probably shaped by the processing of prosodic information, which mainly constitutes newborns\' first experiences of speech in the womb. More exposure to segmental information after birth may lead to strengthened functional connectivities in the language system in both groups, resulting in a stronger leftward lateralization in males and a more balanced or leftward dominance in females.

BACKGROUND: Observational assessments of infant temperament have provided unparalleled insight into prediction of risk for social anxiety. However, it is challenging to administer and score these assessments alongside high-quality infant neuroimaging data. In the current study, we aimed to identify infant resting-state functional connectivity associated with both parent report and observed behavioral estimates of infant novelty-evoked distress.
METHODS: Using data from the OIT (Origins of Infant Temperament) study, which includes deep phenotyping of infant temperament, we identified parent-report measures that were associated with observed novelty-evoked distress. These parent-report measures were then summarized into a composite score used for imaging analysis. Our infant magnetic resonance imaging sample was a synthetic cohort, harmonizing data from 2 functional magnetic resonance imaging studies of 4-month-old infants (OIT and BCP [Baby Connectome Project]; n = 101), both of which included measures of parent-reported temperament. Brain-behavior associations were evaluated using enrichment, a statistical approach that quantifies the clustering of brain-behavior associations within network pairs.
RESULTS: Results demonstrated that parent-report composites of novelty-evoked distress were significantly associated with 3 network pairs: dorsal attention-salience/ventral attention, dorsal attention-default mode, and dorsal attention-control. These network pairs demonstrated negative associations with novelty-evoked distress, indicating that less connectivity between these network pairs was associated with greater novelty-evoked distress. Additional analyses demonstrated that dorsal attention-control network connectivity was associated with observed novelty-evoked distress in the OIT sample (n = 38).
CONCLUSIONS: Overall, this work is broadly consistent with existing work and implicates dorsal attention network connectivity in novelty-evoked distress. This study provides novel data on the neural basis of infant novelty-evoked distress.

OBJECTIVE: We aimed to explore structural connectivity in status epilepticus.
METHODS: We enrolled participants who underwent diffusion tensor imaging. We applied graph theory to investigate structural connectivity. We compared the structural connectivity measures between patients and healthy controls and between patients with poor (modified Rankin Scale [mRS] >3) and good (mRS ≤3) admission outcomes.
RESULTS: We enrolled 28 patients and 31 healthy controls (age 65.5 vs.62.0 years, p = .438). Of these patients, 16 and 12 showed poor and good admission outcome (age 65.5 vs.62.0 years, p = .438). The assortative coefficient (-0.113 vs. -0.121, p = .021), mean clustering coefficient (0.007 vs.0.006, p = .009), global efficiency (0.023 vs.0.020, p = .009), transitivity (0.007 vs.0.006, p = .009), and small-worldness index (0.006 vs.0.005, p = .021) were higher in patients with status epilepticus than in healthy controls. The assortative coefficient (-0.108 vs. -0.119, p = .042), mean clustering coefficient (0.007 vs.0.006, p = .042), and transitivity (0.008 vs.0.007, p = .042) were higher in patients with poor admission outcome than in those with good admission outcome. MRS score was positively correlated with structural connectivity measures, including the assortative coefficient (r = 0.615, p = .003), mean clustering coefficient (r = 0.544, p = .005), global efficiency (r = 0.515, p = .007), transitivity (r = 0.547, p = .007), and small-worldness index (r = 0.435, p = .024).
CONCLUSIONS: We revealed alterations in structural connectivity, showing increased integration and segregation in status epilepticus, which might be related with neuronal synchronization. This effect was more pronounced in patients with a poor admission outcome, potentially reshaping our understanding for comprehension of status epilepticus mechanisms and the development of more targeted treatments.

BACKGROUND: Primary lateral sclerosis (PLS) is traditionally solely associated with progressive upper motor neuron dysfunction manifesting in limb spasticity, gait impairment, bulbar symptoms and pseudobulbar affect. Recent studies have described frontotemporal dysfunction in some patients resulting in cognitive manifestations. Cerebellar pathology is much less well characterised despite sporadic reports of cerebellar disease.
METHODS: A multi-timepoint, longitudinal neuroimaging study was conducted to characterise the evolution of both intra-cerebellar disease burden and cerebro-cerebellar connectivity. The volumes of deep cerebellar nuclei, cerebellar cortical volumes, cerebro-cerebellar structural and functional connectivity were assessed longitudinally in a cohort of 43 individuals with PLS.
RESULTS: Cerebello-frontal, -temporal, -parietal, -occipital and cerebello-thalamic structural disconnection was detected at baseline based on radial diffusivity (RD) and cerebello-frontal decoupling was also evident based on fractional anisotropy (FA) alterations. Functional connectivity changes were also detected in cerebello-frontal, parietal and occipital projections. Volume reductions were identified in the vermis, anterior lobe, posterior lobe, and crura. Among the deep cerebellar nuclei, the dorsal dentate was atrophic. Longitudinal follow-up did not capture statistically significant progressive changes. Significant primary motor cortex atrophy and inter-hemispheric transcallosal degeneration were also captured.
CONCLUSIONS: PLS is not only associated with upper motor neuron dysfunction, but cerebellar cortical volume loss and deep cerebellar nuclear atrophy can also be readily detected. In addition to intra-cerebellar disease burden, cerebro-cerebellar connectivity alterations also take place. Our data add to the evolving evidence of widespread neurodegeneration in PLS beyond the primary motor regions. Cerebellar dysfunction in PLS is likely to exacerbate bulbar, gait and dexterity impairment and contribute to pseudobulbar affect.

UNASSIGNED: Structural brain connectivity abnormalities have been associated with several psychiatric disorders. Schizophrenia (SCZ) is a chronic disabling disorder associated with accelerated aging and increased risk of dementia, though brain findings in the disorder have rarely been directly compared to those that occur with aging.
UNASSIGNED: We used an automated approach to reconstruct key white matter tracts and assessed tract integrity in five participant groups. We acquired one-hour-long high-directional diffusion MRI data from young control (CON, n =28), bipolar disorder (BPD, n =21), and SCZ (n =22) participants aged 18-30, and healthy elderly (ELD, n =15) and dementia (DEM, n =9) participants. Volume, fractional (FA), radial diffusivity (RD) and axial diffusivity (AD) of seven key white matter tracts (anterior thalamic radiation, ATR; dorsal and ventral cingulum bundle, CBD and CBV; corticospinal tract, CST; and the three superior longitudinal fasciculi: SLF-1, SLF-2 and SLF-3) were analyzed with TRACULA. Group comparisons in tract metrics were performed using multivariate and univariate analyses. Clinical relationships of tract metrics with recent and chronic symptoms were assessed in SCZ and BPD participants.
UNASSIGNED: A MANOVA showed group differences in FA (λ=0.5; p=0.0002) and RD (λ=0.35; p<0.0001) across the seven tracts, but no significant differences in tract AD and volume. Post-hoc analyses indicated lower tract FA and higher RD in ELD and DEM groups compared to CON, BPD and SCZ groups. Lower FA and higher RD in SCZ compared to CON did not meet statistical significance. In SCZ participants, a significant negative correlation was found between chronic psychosis severity and FA in the SLF-1 (r= -0.45; p=0.035), SLF-2 (r= -0.49; p=0.02) and SLF-3 (r= -0.44; p=0.042).
UNASSIGNED: Our results indicate impaired white matter tract integrity in elderly populations consistent with myelin damage. Impaired tract integrity in SCZ is most prominent in patients with advanced illness.

UNASSIGNED: Rumination-focused cognitive behavioral therapy (RF-CBT) is designed to reduce depressive rumination or the habitual tendency to dwell on experiences in a repetitive, negative, passive, and global manner. RF-CBT uses functional analysis, experiential exercises, and repeated practice to identify and change the ruminative habit. This preregistered randomized clinical trial (NCT03859297, R61) is a preregistered replication of initial work. We hypothesized a concurrent reduction of both self-reported rumination and cross-network connectivity between the left posterior cingulate cortex and right inferior frontal and inferior temporal gyri.
UNASSIGNED: Seventy-six youths with a history of depression and elevated rumination were randomized to 10 to 14 sessions of RF-CBT (n = 39; 34 completers) or treatment as usual (n = 37; 28 completers). Intent-to-treat analyses assessed pre-post change in rumination response scale and in functional connectivity assessed using two 5 minute, 12 second runs of resting-state functional magnetic resonance imaging.
UNASSIGNED: We replicated previous findings: a significant reduction in rumination response scale and a reduction in left posterior cingulate cortex to right inferior frontal gyrus/inferior temporal gyrus connectivity in participants who received RF-CBT compared with those who received treatment as usual. Reductions were large (z change = 0.84; 0.73, respectively [ps < .05]).
UNASSIGNED: This adolescent clinical trial further demonstrates that depressive rumination is a brain-based mechanism that is modifiable via RF-CBT. Here, we replicated that RF-CBT reduces cross-network connectivity, a possible mechanism by which rumination becomes less frequent, intense, and automatic. This National Institute of Mental Health-funded fast-fail study continues to the R33 phase during which treatment-specific effects of RF-CBT will be compared with relaxation therapy.

A novel method has been developed for analyzing connectivity between regions based on functional magnetic resonance imaging (fMRI) data. This method, termed structural and physiological modeling (SAPM), combines information about blood oxygenation-level dependent (BOLD) responses, anatomy, and physiology to model coordinated signaling across networks of regions, including input and output signaling from each region and whether signaling is predominantly inhibitory or excitatory. The present study builds on a prior proof-of-concept demonstration of the SAPM method by providing evidence for the choice of network model and anatomical sub-regions, demonstrating the reproducibility of the results and identifying statistical thresholds needed to infer significance. The method is further validated by applying it to investigate human nociceptive processing in the brainstem and spinal cord and comparing the results to the known neuroanatomy, including anatomical regions and inhibitory and excitatory signaling. The results of this analysis demonstrate that it is possible to obtain reliable information about input and output signaling from anatomical regions and to identify whether this signaling has predominantly inhibitory or excitatory effects. SAPM provides much more detailed information about neuroanatomy than was previously possible based on fMRI data.