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Autism spectrum disorders (ASDs) are characterized by deficits in social and communication processes. Recent data suggest that altered functional connectivity (FC), i.e. synchronous brain activity, might contribute to these deficits. Of specific interest is the FC integrity of the default mode network (DMN), a network active during passive resting states and cognitive processes related to social deficits seen in ASD, e.g. Theory of Mind. We investigated the role of altered FC of default mode sub-networks (DM-SNs) in 16 patients with high-functioning ASD compared to 16 matched healthy controls of short resting fMRI scans using independent component analysis (ICA). ICA is a multivariate data-driven approach that identifies temporally coherent networks, providing a natural measure of FC. Results show that compared to controls, patients showed decreased FC between the precuneus and medial prefrontal cortex/anterior cingulate cortex, DMN core areas, and other DM-SNs areas. FC magnitude in these regions inversely correlated with the severity of patients' social and communication deficits as measured by the Autism Diagnostic Observational Schedule and the Social Responsiveness Scale. Importantly, supplemental analyses suggest that these results were independent of treatment status. These results support the hypothesis that DM-SNs under-connectivity contributes to the core deficits seen in ASD. Moreover, these data provide further support for the use of data-driven analysis with resting-state data for illuminating neural systems that differ between groups. This approach seems especially well suited for populations where compliance with and performance of active tasks might be a challenge, as it requires minimal cooperation. ► Three default mode sub-networks (DM-SNs) were identified from resting state fMRI scans of 16 patients with high-functioning autism spectrum disorders (ASD) and 16 matched healthy controls (HC) using independent component analysis (ICA). ► Compared to HC, patients with ASD showed decreased functional connectivity between the precuneus and medial prefrontal cortex/anterior cingulate cortex and other DM-SNs areas. ► FC magnitude in these regions inversely correlated with the severity of patients’ social and communication deficits as measured by the Autism Diagnostic Observational Schedule and the Social Responsiveness Scale. ► Supplemental analyses suggest that these results were independent of treatment status.

There are changes to the degree of cortical folding from gestation through adolescence into young adulthood. Recent evidence suggests that degree of cortical folding is linked to individual differences in general cognitive ability in healthy adults. However, it is not yet known whether age-related cortical folding changes are related to maturation of specific cognitive abilities in adolescence. To address this, we examined the relationship between frontoparietal cortical folding as measured by a Freesurfer-derived local gyrification index (lGI) and performance on subtests from the Wechsler Abbreviated Scale of Intelligence and scores from Conner's Continuous Performance Test-II in 241 healthy adolescents (ages 12–25 years). We hypothesized that age-related lGI changes in the frontoparietal cortex would contribute to cognitive development. A secondary goal was to explore if any gyrification-cognition relationships were either test-specific or sex-specific. Consistent with previous studies, our results showed a reduction of frontoparietal local gyrification with age. Also, as predicted, all cognitive test scores (i.e., Vocabulary, Matrix Reasoning, the CPT-II Commission, Omission, Variabiltiy, d’) showed age × cognitive ability interaction effects in frontoparietal and temporoparietal brain regions. Mediation analyses confirmed a causal role of age-related cortical folding changes only for CPT-II Commission errors. Taken together, the results support the functional significance of cortical folding, as well as provide the first evidence that cortical folding maturational changes play a role in cognitive development.

Skin conductance responses (SCRs) reliably occur in the absence of external stimulation. However, the neural correlates of these non-specific SCRs have been less explored than brain activity associated with stimulus-elicited SCRs. This study modeled spontaneous skin conductance responses observed during an unstructured resting state fMRI scan in 58 adolescents. A Finite Impulse Response (FIR) fMRI model was used to detect any type of hemodynamic response shape time-locked to non-specific SCRs; the shape of these responses was then carefully characterized. The strongest evidence for signal change was found in several sub-regions of sensorimotor cortex. There also was evidence for engagement of discrete areas within the lateral surfaces of the parietal lobe, cingulate cortex, fronto-insular operculum, and both visual and auditory primary processing areas. The hemodynamic profile measured by FIR modeling clearly resembled an event-related response. However, it was a complex response, best explained by two quickly successive, but opposing neuronal impulses across all brain regions – a brief positive response that begins several seconds prior to the SCR with a much longer negative neuronal impulse beginning shortly after the SCR onset. Post hoc exploratory analyses linked these two hemodynamic response phases to different emotion-related individual differences. In conclusion, this study shows the neural correlates of non-specific SCRs are a widespread, cortical network of brain regions engaged in a complex, seemingly biphasic fashion. This bimodal response profile should be considered in replication studies that attempt to directly link brain activity to possible homeostatic mechanisms or seek evidence for alternative mechanisms. •Neural correlates of spontaneous nonspecific SCRs is a widespread cortical network.•This network displayed a seemingly biphasic event related hemodynamic response.•Both phases of the hemodynamic response correlated with behavioral measures.•Studies are needed to replicate this response and explore neural mechanisms.

Background Time to diagnosis (TTD) concerns teenagers and young adults (TYA) with cancer and may affect outcome. Methods Healthcare records from 105 TYA in a regional cancer service were assessed to document events from 1st symptom to treatment start. Detailed pathway construction was possible for 104 patients and allowed a multidisciplinary panel review of each pathway with assessment of good practice and lessons for the future. Results 1st presentation was to primary care in 86, and 93% consulted in primary care before diagnosis. Routes to Diagnosis were 45% via urgent 2 Week Wait pathways and 38% as emergency referrals. Total Interval (time from 1st presentation to treatment start) was median 63 (range 1–559) days, varying within/between diagnoses. Patient interval (time from 1st symptom to 1st presentation) was longest for lymphoma, carcinoma and bone tumour (medians: 9, 12, 20 days). Overall, time in primary care was short (median 3, range 0–537 days) compared to secondary care (median 29, range 0–195 days) and longest for lymphoma, carcinoma, brain/CNS (medians: 10, 15, 16 days). Specialist Care interval (time from 1st specialist visit to treatment start) was longest for bone, brain/CNS, lymphoma, carcinoma (medians: 30, 33, 36, 48 days). 40% pathways were rated as showing good/best practice but 16% were less than satisfactory. Continued safety-netting/support was identified from primary care but analysis suggested opportunities for improvement in transition through secondary care. Conclusions Previous reports of prolonged TTD have focused on delay in referral from primary care but this study suggests that this might be reduced by optimising management in secondary care.

We present an exploratory cross-sectional analysis of the effect of season and weather on Freesurfer-derived brain volumes from a sample of 3,279 healthy individuals collected on two MRI scanners in Hartford, CT, USA over a 15 year period. Weather and seasonal effects were analyzed using a single linear regression model with age, sex, motion, scan sequence, time-of-day, month of the year, and the deviation from average barometric pressure, air temperature, and humidity, as covariates. FDR correction for multiple comparisons was applied to groups of non-overlapping ROIs. Significant negative relationships were found between the left- and right- cerebellum cortex and pressure (t = -2.25, p = 0.049; t = -2.771, p = 0.017). Significant positive relationships were found between left- and right- cerebellum cortex and white matter between the comparisons of January/June and January/September. Significant negative relationships were found between several subcortical ROIs for the summer months compared to January. An opposing effect was observed between the supra- and infra-tentorium, with opposite effect directions in winter and summer. Cohen’s d effect sizes from monthly comparisons were similar to those reported in recent psychiatric big-data publications, raising the possibility that seasonal changes and weather may be confounds in large cohort studies. Additionally, changes in brain volume due to natural environmental variation have not been reported before and may have implications for weather-related and seasonal ailments.

Numerous neuroimaging studies report abnormal regional brain activity during working memory performance in schizophrenia, but few have examined brain network integration as determined by \"functional connectivity\" analyses. We used independent component analysis (ICA) to identify and characterize dysfunctional spatiotemporal networks in schizophrenia engaged during the different stages (encoding and recognition) of a Sternberg working memory fMRI paradigm. 37 chronic schizophrenia and 54 healthy age/gender-matched participants performed a modified Sternberg Item Recognition fMRI task. Time series images preprocessed with SPM2 were analyzed using ICA. Schizophrenia patients showed relatively less engagement of several distinct \"normal\" encoding-related working memory networks compared to controls. These encoding networks comprised 1) left posterior parietal-left dorsal/ventrolateral prefrontal cortex, cingulate, basal ganglia, 2) right posterior parietal, right dorsolateral prefrontal cortex and 3) default mode network. In addition, the left fronto-parietal network demonstrated a load-dependent functional response during encoding. Network engagement that differed between groups during recognition comprised the posterior cingulate, cuneus and hippocampus/parahippocampus. As expected, working memory task accuracy differed between groups (p<0.0001) and was associated with degree of network engagement. Functional connectivity within all three encoding-associated functional networks correlated significantly with task accuracy, which further underscores the relevance of abnormal network integration to well-described schizophrenia working memory impairment. No network was significantly associated with task accuracy during the recognition phase. This study extends the results of numerous previous schizophrenia studies that identified isolated dysfunctional brain regions by providing evidence of disrupted schizophrenia functional connectivity using ICA within widely-distributed neural networks engaged for working memory cognition.

Although reward processing appears altered in addiction, few studies track neurofunctional changes following treatment or relate these to measures of reduced drug use. The current study examined neurofunctional alterations in reward processing in cocaine dependence (CD) pretreatment and posttreatment to determine whether these changes relate to clinically meaningful outcome indicators. Treatment-seeking CD outpatients (N=29) underwent functional magnetic resonance imaging while performing a monetary incentive delay task (MIDT) pretreatment and posttreatment. The MIDT parses anticipatory from outcome phases of reward/loss processing. Abstinence indicators (negative urines, days abstinent from cocaine during follow-up) were collected throughout treatment and up to 1 year later. Healthy control (HC) participants (N=28) were also scanned twice with the MIDT. Relative to pretreatment, at posttreatment CD participants demonstrated increased anticipatory reward activity in the midbrain, thalamus, and precuneus (pFWE<0.05). Increased midbrain activity correlated with cocaine abstinence during the 1-year follow-up. Ventral striatal (VS) activity during loss anticipation correlated negatively with negative urine screens. HC group test-retest results showed decreased ventromedial prefrontal cortex activity during winning outcomes. CD-HC group-by-time differences revealed increased left inferior frontal gyrus activity in the CD group during anticipatory phases at posttreatment. In CD participants, increased posttreatment activity in dopamine-innervated regions suggests lowered thresholds in anticipatory signaling for non-drug rewards. Midbrain and VS responses may represent biomarkers associated with CD abstinence. Abstinence-related neurobiological changes occur in similar regions implicated during active use and may possibly be used to track progress during short- and long-term recovery.

Several reports show that traumatic brain injury (TBI) results in abnormalities in the coordinated activation among brain regions. Because most previous studies examined moderate/severe TBI, the extensiveness of functional connectivity abnormalities and their relationship to postconcussive complaints or white matter microstructural damage are unclear in mild TBI. This study characterized widespread injury effects on multiple integrated neural networks typically observed during a task-unconstrained “resting state” in mild TBI patients. Whole brain functional connectivity for twelve separate networks was identified using independent component analysis (ICA) of fMRI data collected from thirty mild TBI patients mostly free of macroscopic intracerebral injury and thirty demographically-matched healthy control participants. Voxelwise group comparisons found abnormal mild TBI functional connectivity in every brain network identified by ICA, including visual processing, motor, limbic, and numerous circuits believed to underlie executive cognition. Abnormalities not only included functional connectivity deficits, but also enhancements possibly reflecting compensatory neural processes. Postconcussive symptom severity was linked to abnormal regional connectivity within nearly every brain network identified, particularly anterior cingulate. A recently developed multivariate technique that identifies links between whole brain profiles of functional and anatomical connectivity identified several novel mild TBI abnormalities, and represents a potentially important new tool in the study of the complex neurobiological sequelae of TBI.

The underlying genetic etiology of late onset Alzheimer's disease (LOAD) remains largely unknown, likely due to its polygenic architecture and a lack of sophisticated analytic methods to evaluate complex genotype–phenotype models. The aim of the current study was to overcome these limitations in a bi-multivariate fashion by linking intermediate magnetic resonance imaging (MRI) phenotypes with a genome-wide sample of common single nucleotide polymorphism (SNP) variants. We compared associations between 94 different brain regions of interest derived from structural MRI scans and 533,872 genome-wide SNPs using a novel multivariate statistical procedure, parallel-independent component analysis, in a large, national multi-center subject cohort. The study included 209 elderly healthy controls, 367 subjects with amnestic mild cognitive impairment and 181 with mild, early-stage LOAD, all of them Caucasian adults, from the Alzheimer's Disease Neuroimaging Initiative cohort. Imaging was performed on comparable 1.5T scanners at over 50 sites in the USA/Canada. Four primary “genetic components” were associated significantly with a single structural network including all regions involved neuropathologically in LOAD. Pathway analysis suggested that each component included several genes already known to contribute to LOAD risk (e.g. APOE4) or involved in pathologic processes contributing to the disorder, including inflammation, diabetes, obesity and cardiovascular disease. In addition significant novel genes identified included ZNF673, VPS13, SLC9A7, ATP5G2 and SHROOM2. Unlike conventional analyses, this multivariate approach identified distinct groups of genes that are plausibly linked in physiologic pathways, perhaps epistatically. Further, the study exemplifies the value of this novel approach to explore large-scale data sets involving high-dimensional gene and endophenotype data.

Previous studies of brain structure abnormalities in conduct disorder and attention-deficit/hyperactivity disorder (ADHD) samples have been limited owing to cross-comorbidity, preventing clear understanding of which structural brain abnormalities might be specific to or shared by each disorder. To our knowledge, this study was the first direct comparison of grey and white matter volumes in diagnostically “pure” (i.e., no comorbidities) conduct disorder and ADHD samples. Groups of adolescents with noncormobid conduct disorder and with noncomorbid, combined-subtype ADHD were compared with age- and sex-matched controls using DARTEL voxel-based analysis of T1-weighted brain structure images. Analysis of variance with post hoc analyses compared whole brain grey and white matter volumes among the groups. We included 24 adolescents in each study group. There was an overall 13% reduction in grey matter volume in adolescents with conduct disorder, reflecting numerous frontal, temporal, parietal and subcortical deficits. The same grey matter regions typically were not abnormal in those with ADHD. Deficits in frontal lobe regions previously identified in studies of patients with ADHD either were not detected, or group differences from controls were not as strong as those between the conduct disorder and control groups. White matter volume measurements did not differentiate conduct disorder and ADHD. Our modest sample sizes prevented meaningful examination of individual features of ADHD or conduct disorder, such as aggression, callousness, or hyperactive versus inattentive symptom subtypes. The evidence supports theories of frontotemporal abnormalities in adolescents with conduct disorder, but raises questions about the prominence of frontal lobe and striatal structural abnormalities in those with noncomorbid, combined-subtype ADHD. The latter point is clinically important, given the widely held belief that ADHD is associated with numerous frontal lobe structural deficits, a conclusion that is not strongly supported following direct comparison of diagnostically pure groups. The results are important for future etiological studies, particularly those seeking to identify how early expression of specific brain structure abnormalities could potentiate the risk for antisocial behaviour.