Functional connectivity and temporal variability of brain connections in adults with attention deficit/hyperactivity disorder and bipolar disorder.

OBJECTIVES: To assess brain functional connectivity and variability in adults with attention deficit/hyperactivity disorder (ADHD) or euthymic bipolar disorder (BD) relative to a control (CT) group. METHODS: Electroencephalography (EEG) was measured in 35 participants (BD = 11; ADHD = 9; CT = 15) during an eyes-closed 10-min rest period, and connectivity and graph theory metrics were computed. A coefficient of variation (CV) computed also the connectivity's temporal variability of EEG. Multivariate associations between functional connectivity and clinical and neuropsychological profiles were evaluated. RESULTS: An enhancement of functional connectivity was observed in the ADHD (fronto-occipital connections) and BD (diffuse connections) groups. However, compared with CTs, intrinsic variability (CV) was enhanced in the ADHD group and reduced in the BD group. Graph theory metrics confirmed the existence of several abnormal network features in both affected groups. Significant associations of connectivity with symptoms were also observed. In the ADHD group, temporal variability of functional connections was associated with executive function and memory deficits. Depression, hyperactivity and impulsivity levels in the ADHD group were associated with abnormal intrinsic connectivity. In the BD group, levels of anxiety and depression were related to abnormal frontotemporal connectivity. CONCLUSIONS: In the ADHD group, we found that intrinsic variability was associated with deficits in cognitive performance and that connectivity abnormalities were related to ADHD symptomatology. The BD group exhibited less intrinsic variability and more diffuse long-range brain connections, and those abnormalities were related to interindividual differences in depression and anxiety. These preliminary results are relevant for neurocognitive models of abnormal brain connectivity in both disorders.

The Learning Disabilities Network (LeaDNet): using neurofibromatosis type 1 (NF1) as a paradigm for translational research.

Learning disabilities and other cognitive disorders represent one of the most important unmet medical needs and a significant source of lifelong disability. To accelerate progress in this area, an international consortium of researchers and clinicians, the Learning Disabilities Network (LeaDNet), was established in 2006. Initially, LeaDNet focused on neurofibromatosis type 1 (NF1), a common single gene disorder with a frequency of 1:3,000. Although NF1 is best recognized as an inherited tumor predisposition syndrome, learning, cognitive, and neurobehavioral deficits account for significant morbidity in this condition and can have a profound impact on the quality of life of affected individuals. Recently, there have been groundbreaking advances in our understanding of the molecular, cellular, and neural systems underpinnings of NF1-associated learning deficits in animal models, which precipitated clinical trials using a molecularly targeted treatment for these deficits. However, much remains to be learned about the spectrum of cognitive, neurological, and psychiatric phenotypes associated with the NF1 clinical syndrome. In addition, there is a pressing need to accelerate the identification of specific clinical targets and treatments for these phenotypes. The successes with NF1 have allowed LeaDNet investigators to broaden their initial focus to other genetic disorders characterized by learning disabilities and cognitive deficits including other RASopathies (caused by changes in the Ras signaling pathway). The ultimate mission of LeaDNet is to leverage an international translational consortium of clinicians and neuroscientists to integrate bench-to-bedside knowledge across a broad range of cognitive genetic disorders, with the goal of accelerating the development of rational and biologically based treatments.

Abnormal brain connectivity patterns in adults with ADHD: a coherence study.

Studies based on functional magnetic resonance imaging (fMRI) during the resting state have shown decreased functional connectivity between the dorsal anterior cingulate cortex (dACC) and regions of the Default Mode Network (DMN) in adult patients with Attention-Deficit/Hyperactivity Disorder (ADHD) relative to subjects with typical development (TD). Most studies used Pearson correlation coefficients among the BOLD signals from different brain regions to quantify functional connectivity. Since the Pearson correlation analysis only provides a limited description of functional connectivity, we investigated functional connectivity between the dACC and the posterior cingulate cortex (PCC) in three groups (adult patients with ADHD, n=21; TD age-matched subjects, n=21; young TD subjects, n=21) using a more comprehensive analytical approach - unsupervised machine learning using a one-class support vector machine (OC-SVM) that quantifies an abnormality index for each individual. The median abnormality index for patients with ADHD was greater than for TD age-matched subjects (p=0.014); the ADHD and young TD indices did not differ significantly (p=0.480); the median abnormality index of young TD was greater than that of TD age-matched subjects (p=0.016). Low frequencies below 0.05 Hz and around 0.20 Hz were the most relevant for discriminating between ADHD patients and TD age-matched controls and between the older and younger TD subjects. In addition, we validated our approach using the fMRI data of children publicly released by the ADHD-200 Competition, obtaining similar results. Our findings suggest that the abnormal coherence patterns observed in patients with ADHD in this study resemble the patterns observed in young typically developing subjects, which reinforces the hypothesis that ADHD is associated with brain maturation deficits.