The aim of the present study is to investigate the brain circuits or networks that underpin diagnostically specific tasks by means of group independent component analysis for FMRI toolbox (GIFT). We hypothesised that there will be neural network patterns of activation and deactivation, which correspond to real-time performance on clinical self-evaluation scales.

In total, 20 healthy controls (HC) and 22 patients with major depressive episode have been included. All subjects were scanned with functional magnetic resonance imaging (fMRI) with paradigm composed of diagnostic clinical self-assessment depression scale contrasted to neutral scale. The data were processed with group independent component analysis for functional MRI toolbox and statistical parametric mapping.

The results have demonstrated that there exist positively or negatively modulated brain networks during processing of diagnostic specific task questions for depressive disorder. There have also been confirmed differences in the networks processing diagnostic versus off blocks between patients and controls in anterior cingulate cortex and middle frontal gyrus. Diagnostic conditions (depression scale) when contrasted to neutral conditions demonstrate differential activity of right superior frontal gyrus and right middle cingulate cortex in the comparison of patients with HC.

Potential neuroimaging of state-dependent biomarkers has been directly linked with clinical assessment self-evaluation scale, administered as stimuli simultaneously with the fMRI acquisition. It may be regarded as further evidence in support of the convergent capacity of both methods to distinguish groups by means of incremental translational cross-validation.

The Cognitive Battery of the National Institutes of Health Toolbox (NIH-TB) is a collection of assessments that have been adapted and normed for administration across the lifespan and is increasingly used in large-scale population-level research. However, despite increasing adoption in longitudinal investigations of neurocognitive development, and growing recommendations that the Toolbox be used in clinical applications, little is known about the long-term temporal stability of the NIH-TB, particularly in youth.

The present study examined the long-term temporal reliability of the NIH-TB in a large cohort of youth (9–15 years-old) recruited across two data collection sites. Participants were invited to complete testing annually for 3 years.

Reliability was generally low-to-moderate, with intraclass correlation coefficients ranging between 0.31 and 0.76 for the full sample. There were multiple significant differences between sites, with one site generally exhibiting stronger temporal stability than the other.

Reliability of the NIH-TB Cognitive Battery was lower than expected given early work examining shorter test-retest intervals. Moreover, there were very few instances of tests meeting stability requirements for use in research; none of the tests exhibited adequate reliability for use in clinical applications. Reliability is paramount to establishing the validity of the tool, thus the constructs assessed by the NIH-TB may vary over time in youth. We recommend further refinement of the NIH-TB Cognitive Battery and its norming procedures for children before further adoption as a neuropsychological assessment. We also urge researchers who have already employed the NIH-TB in their studies to interpret their results with caution.

Schizophrenia is associated with robust hippocampal volume deficits but subregion volume deficits, their associations with cognition, and contributing genes remain to be determined.

Hippocampal formation (HF) subregion volumes were obtained using FreeSurfer 6.0 from individuals with schizophrenia (n = 176, mean age ± s.d. = 39.0 ± 11.5, 132 males) and healthy volunteers (n = 173, mean age ± s.d. = 37.6 ± 11.3, 123 males) with similar mean age, gender, handedness, and race distributions. Relationships between the HF subregion volume with the largest between group difference, neuropsychological performance, and single-nucleotide polymorphisms were assessed.

This study found a significant group by region interaction on hippocampal subregion volumes. Compared to healthy volunteers, individuals with schizophrenia had significantly smaller dentate gyrus (DG) (Cohen's d = −0.57), Cornu Ammonis (CA) 4, molecular layer of the hippocampus, hippocampal tail, and CA 1 volumes, when statistically controlling for intracranial volume; DG (d = −0.43) and CA 4 volumes remained significantly smaller when statistically controlling for mean hippocampal volume. DG volume showed the largest between group difference and significant positive associations with visual memory and speed of processing in the overall sample. Genome-wide association analysis with DG volume as the quantitative phenotype identified rs56055643 (β = 10.8, p < 5 × 10−8, 95% CI 7.0–14.5) on chromosome 3 in high linkage disequilibrium with MOBP. Gene-based analyses identified associations between SLC25A38 and RPSA and DG volume.

This study suggests that DG dysfunction is fundamentally involved in schizophrenia pathophysiology, that it may contribute to cognitive abnormalities in schizophrenia, and that underlying biological mechanisms may involve contributions from MOBP, SLC25A38, and RPSA.

Studies have produced conflicting evidence regarding whether cognitive control deficits in patients with schizophrenia result from dysfunction within the cognitive control network (CCN; top-down) and/or unisensory cortex (bottom-up).

To investigate CCN and sensory cortex involvement during multisensory cognitive control in patients with schizophrenia.

Patients with schizophrenia and healthy controls underwent functional magnetic resonance imaging while performing a multisensory Stroop task involving auditory and visual distracters.

Patients with schizophrenia exhibited an overall pattern of response slowing, and these behavioural deficits were associated with a pattern of patient hyperactivation within auditory, sensorimotor and posterior parietal cortex. In contrast, there were no group differences in functional activation within prefrontal nodes of the CCN, with small effect sizes observed (incongruent–congruent trials). Patients with schizophrenia also failed to upregulate auditory cortex with concomitant increased attentional demands.

Results suggest a prominent role for dysfunction within auditory, sensorimotor and parietal areas relative to prefrontal CCN nodes during multisensory cognitive control.

Virtual reality in the form of simulated driving is a useful tool for studying the brain. Various clinical questions can be addressed, including both the role of alcohol as a modulator of brain function and regional brain activation related to elements of driving.

We reviewed a study of the neural correlates of alcohol intoxication through the use of a simulated-driving paradigm and wished to demonstrate the utility of recording continuous-driving behavior through a new study using a programmable driving simulator developed at our center.

Functional magnetic resonance imaging data was collected from subjects while operating a driving simulator. Independent component analysis (ICA) was used to analyze the data. Specific brain regions modulated by alcohol, and relationships between behavior, brain function, and alcohol blood levels were examined with aggregate behavioral measures. Fifteen driving epochs taken from two subjects while also recording continuously recorded driving variables were analyzed with ICA.

Preliminary findings reveal that four independent components correlate with various aspects of behavior. An increase in braking while driving was found to increase activation in motor areas, while cerebellar areas showed signal increases during steering maintenance, yet signal decreases during steering changes. Additional components and significant findings are further outlined.

In summary, continuous behavioral variables conjoined with ICA may offer new insight into the neural correlates of complex human behavior.

Aberrant electrophysiological and haemodynamic processing of auditory oddball stimuli is among the most robustly documented findings in patients with schizophrenia. However, no study to date has directly examined linked patterns of electrical and haemodynamic differences in patients and controls.

In a recent paper we demonstrated a data-driven approach, joint independent component analysis (jICA) to fuse together functional magnetic resonance imaging (fMRI) and event-related potential (ERP) data and elucidated the chronometry of auditory oddball target detection in healthy control subjects. In this paper we extend our fusion method to identify specific differences in the neuronal chronometry of target detection for chronic schizophrenia patients compared to healthy controls.

We found one linked source, consistent with the N2 response, known to be related to cognitive processing of deviant stimuli, spatially localized to bilateral fronto-temporal regions. This source showed significant between-group differences both in amplitude response and in the fMRI/ERP distribution pattern. These findings are consistent with previous work showing N2 amplitude and latency abnormalities in schizophrenia, and provide new information about the linkage between the two.

In summary, we use a novel approach to isolate and identify a linked fMRI/ERP component which shows marked differences in chronic schizophrenia patients. We also show that jointly using both fMRI and ERP measures provides a fully picture of the underlying haemodynamic and electrical changes which are present in patients. Our approach also has broad applicability to other diseases such as autism, Alzheimer's disease, or bipolar disorder.