BackgroundMood disorders are common and associated with significant morbidity and mortality. Better tools are needed for their diagnosis and treatment. Deeper phenotypic understanding of these disorders is integral to the development of such tools. This study is the first effort to use passively collected mobile phone keyboard activity to build deep digital phenotypes of depression and mania.ObjectiveThe objective of our study was to investigate the relationship between mobile phone keyboard activity and mood disturbance in subjects with bipolar disorders and to demonstrate the feasibility of using passively collected mobile phone keyboard metadata features to predict manic and depressive signs and symptoms as measured via clinician-administered rating scales.MethodsUsing a within-subject design of 8 weeks, subjects were provided a mobile phone loaded with a customized keyboard that passively collected keystroke metadata. Subjects were administered the Hamilton Depression Rating Scale (HDRS) and Young Mania Rating Scale (YMRS) weekly. Linear mixed-effects models were created to predict HDRS and YMRS scores. The total number of keystrokes was 626,641, with a weekly average of 9791 (7861), and that of accelerometer readings was 6,660,890, with a weekly average 104,076 (68,912).ResultsA statistically significant mixed-effects regression model for the prediction of HDRS-17 item scores was created: conditional R2=.63, P=.01. A mixed-effects regression model for YMRS scores showed the variance accounted for by random effect was zero, and so an ordinary least squares linear regression model was created: R2=.34, P=.001. Multiple significant variables were demonstrated for each measure.ConclusionsMood states in bipolar disorder appear to correlate with specific changes in mobile phone usage. The creation of these models provides evidence for the feasibility of using passively collected keyboard metadata to detect and monitor mood disturbances.

Previous findings suggested the role of the prefrontal cortex, hippocampus, and cingulate gyrus in major depressive disorders (MDD), but the white matter microstructural abnormalities of the fibers connecting these brain structures are not known. The purpose of this study was to test the hypothesis that white matter abnormalities are present in association fibers of the uncinate fasciculus (UF) and cingulum bundle (CB) among MDD subjects. A total of 21 MDD subjects aged between 30 and 65 years and 21 age-matched healthy controls (HC) were recruited. All subjects were right-handed and without history of diabetes or other cardiac diseases. We extracted quantitative tract-specific measures based on diffusion tensor imaging tractography to examine both diffusivity and geometric properties of the UF and CB. Significantly decreased fractional anisotropy (FA) and increased radial diffusivity of the right UF were observed in MDD patients compared with HC (po0.05), while their geometric characteristics remained relatively unchanged. Among MDD subjects, depression severity had a significant negative correlation with normalized number of fibers (NNF) in the right UF (r ¼ À0.53, p ¼ 0.02). We also found significant age effect (oldoyoung) in HC group and laterality effect (L4R) in both groups in the FA measure of the CB. Our study demonstrates novel findings of white matter microstructural abnormalities of the right UF in MDD. In the MDD group, the severity of depression is associated with reduced NNF in the right UF. These findings have implications for both clinical manifestations of depression as well as its pathophysiology.

Background This represents the first graph theory based brain network analysis study in bipolar disorder, a chronic and disabling psychiatric disorder characterized by severe mood swings. Many imaging studies have investigated white matter in bipolar disorder with results suggesting abnormal white matter structural integrity, particularly in the fronto-limbic and callosal systems. However, many inconsistencies remain in the literature, and no study to-date has conducted brain network analyses using a graph-theoretic approach. Methods We acquired 64-direction diffusion-weighted MRI on 25 euthymic bipolar I disorder subjects and 24 gender and age equivalent healthy subjects. White matter integrity measures including fractional anisotropy and mean diffusivity were compared in the whole brain. Additionally, structural connectivity matrices based on whole brain deterministic tractography were constructed followed by the computation of both global and local brain network measures. We also designed novel metrics to further probe inter-hemispheric integration. Results Network analyses revealed that the bipolar brain networks exhibited significantly longer characteristic path length, lower clustering coefficient, and lower global efficiency relative to those of controls. Further analyses revealed impaired inter-hemispheric but relatively preserved intra-hemispheric integration. These findings were supported by whole brain white matter analyses that revealed significantly lower integrity in the corpus callosum in bipolar subjects. There were also abnormalities in nodal network measures in structures within the limbic system, especially the left hippocampus, the left lateral orbito-frontal cortex, and the bilateral isthmus cingulate. Conclusions These results suggest abnormalities in structural network organization in bipolar disorder, particularly in inter-hemispheric integration and within the limbic system.

BackgroundWhite matter (WM) integrity may represent a shared biomarker for emotional disorders (ED). Aims: To identify transdiagnostic biomarkers of reduced WM by meta-analysis of findings across multiple EDs.MethodWeb of Science was searched systematically for studies of whole brain analysis of fractional anisotropy (FA) in adults with major depressive disorder, bipolar disorder, social anxiety disorder, obsessive-compulsive disorder or posttraumatic stress disorder compared with a healthy control (HC) group. Peak MNI coordinates were extracted from 37 studies of voxel-based analysis (892 HC and 962 with ED) and meta-analyzed using seed-based d Mapping (SDM) Version 4.31. Separate meta-analyses were also conducted for each disorder.ResultsIn the transdiagnostic meta-analysis, reduced FA was identified in ED studies compared to HCs in the left inferior fronto-occipital fasciculus, forceps minor, uncinate fasciculus, anterior thalamic radiation, superior corona radiata, bilateral superior longitudinal fasciculi, and cerebellum. Disorder-specific meta-analyses revealed the OCD group had the most similarities in reduced FA to other EDs, with every cluster of reduced FA overlapping with at least one other diagnosis. The PTSD group was the most distinct, with no clusters of reduced FA overlapping with any other diagnosis. The BD group were the only disorder to show increased FA in any region, and showed a more bilateral pattern of WM changes, compared to the other groups which tended to demonstrate a left lateralized pattern of FA reductions.ConclusionsDistinct diagnostic categories of ED show commonalities in WM tracts with reduced FA when compared to HC, which links brain networks involved in cognitive and affective processing. This meta-analysis facilitates an increased understanding of the biological markers that are shared by these ED.

Type 2 diabetes and major depression are disorders that are mutual risk factors and may share similar pathophysiological mechanisms. To further understand these shared mechanisms, the purpose of our study was to examine the biochemical basis of depression in patients with type 2 diabetes using proton MRS. Patients with type 2 diabetes and major depression (n ¼ 20) were scanned along with patients with diabetes alone (n ¼ 24) and healthy controls (n ¼ 21) on a 1.5 T MRI/MRS scanner. Voxels were placed bilaterally in dorsolateral white matter and the subcortical nuclei region, both areas important in the circuitry of late-life depression. Absolute values of myoinositol, creatine, N-acetyl aspartate, glutamate, glutamine, and choline corrected for CSF were measured using the LC-Model algorithm. Glutamine and glutamate concentrations in depressed diabetic patients were significantly lower (po0.001) in the subcortical regions as compared to healthy and diabetic control subjects. Myo-inositol concentrations were significantly increased (po0.05) in diabetic control subjects and depressed diabetic patients in frontal white matter as compared to healthy controls. These findings have broad implications and suggest that alterations in glutamate and glutamine levels in subcortical regions along with white matter changes in myo-inositol provide important neurobiological substrates of mood disorders.

Objectives Late-life major depression (LLD) is characterized by distinct epidemiological and psychosocial factors, as well as medical co-morbidities that are associated with specific neuroanatomical differences. The purpose of this study was to use interregional correlations of cortical and subcortical volumes to examine cortical-subcortical structural network properties in subjects with LLD compared to healthy comparison subjects. Design Cross-sectional neuroimaging study Setting General community Participants We recruited 73 healthy elderly comparison subjects and 53 subjects with LLD who volunteered in response to advertisements. Measurements Brain network connectivity measures were generated by correlating regional volumes after controlling for age, gender, and intracranial volume using the Brain Connectivity Toolbox (www.brain-connectivity-toolbox.net). Results Results for overall network strength revealed that LLD networks showed a greater magnitude of associations for both positive and negative correlation weights compared to healthy elderly networks. LLD networks also demonstrated alterations in brain network structure when compared to healthy comparison subjects. LLD networks were also more vulnerable to targeted attacks compared to healthy elderly comparison subjects and this vulnerability was attenuated when controlling for white matter alterations. Conclusions Overall, this study demonstrates that cortical-subcortical network properties are altered in LLD and may reflect the underlying neuroanatomical vulnerabilities of the disorder.

In this paper, we describe the first field tests of a home-based sleep monitoring system, the Nightcap, which uses eyelid and body movement sensors to discriminate wake, NREM, and REM sleep automatically. Ten normal young adults were studied in the sleep laboratory and at home to allow comparison of Nightcap-derived measures with those obtained by traditional polysomnography. The agreement between the two techniques was 87% based on 1-min epochs--93% for NREM, 80% for REM, and 72% for wake. When the values for sleep latency, REM latency, wake time, NREM time, and REM time calculated from polysomnograph records were compared with the values calculated from Nightcap data, no significant differences were seen. In cases of extremely poor sleep, objective sleep efficiency estimates correlated well with subjective reports, suggesting that the Nightcap is sensitive to clinically relevant changes in the quality of sleep. This new device should prove useful to researchers wishing to study the psychophysiology and pathophysiology of sleep in more naturalistic and cost-effective paradigms than possible in the traditional sleep laboratory.

Purpose:To examine the volumes of the gray and white matter both globally and regionally in patients diagnosed with type 2 diabetes and controls. Materials and Methods:Our samples were comprised of 26 patients with type 2 diabetes, 26 patients with diabetes and major depressive disorder, and 25 nondiabetic, nondepressed control subjects. All subjects were studied cross-sectionally on a 1.5 T scanner and were recruited from medicine/diabetes clinics. Both gray and white matter volumes were estimated using an automated method, and the prefrontal areas studied included the anterior cingulate, the gyrus rectus, and the orbitofrontal regions. Results:Patients with diabetes, both with and without depression, had smaller total brain gray matter volumes when compared with the control subjects after controlling for age, intracranial volume, and years of education. This group also had smaller gray matter volumes in the anterior cingulate and orbitofrontal regions when compared with the controls after additionally controlling for total gray matter volume. The depressed and nondepressed diabetic groups did not differ on any neuroimaging measure. Cerebrovascular risk factors correlated negatively with gray matter volumes. Conclusion:The findings indicate that type 2 diabetes is associated with specific neuroanatomical abnormalities in the prefrontal gray matter. Vascular disease might contribute to the findings observed in our sample. These observations have implications for the behavioral sequelae of diabetes.