Face recognition mechanisms need to extract information from static and dynamic faces. It has been hypothesized that the analysis of dynamic face attributes is performed by different face areas than the analysis of static facial attributes. To date, there is no evidence for such a division of labor in macaque monkeys. We used fMRI to determine specializations of macaque face areas for motion. Face areas in the fundus of the superior temporal sulcus responded to general object motion; face areas outside of the superior temporal sulcus fundus responded more to facial motion than general object motion. Thus, the macaque face-processing system exhibits regional specialization for facial motion. Human face areas, processing the same stimuli, exhibited specializations for facial motion as well. Yet the spatial patterns of facial motion selectivity differed across species, suggesting that facial dynamics are analyzed differently in humans and macaques.

Chronic pain is a highly prevalent disease with poorly understood pathophysiology. In particular, the brain mechanisms mediating the transition from acute to chronic pain remain largely unknown. Here, we identify a subcortical signature of back pain. Specifically, subacute back pain patients who are at risk for developing chronic pain exhibit a smaller nucleus accumbens volume, which persists in the chronic phase, compared to healthy controls. The smaller accumbens volume was also observed in a separate cohort of chronic low-back pain patients and was associated with dynamic changes in functional connectivity. At baseline, subacute back pain patients showed altered local nucleus accumbens connectivity between putative shell and core, irrespective of the risk of transition to chronic pain. At follow-up, connectivity changes were observed between nucleus accumbens and rostral anterior cingulate cortex in the patients with persistent pain. Analysis of the power spectral density of nucleus accumbens resting-state activity in the subacute and chronic back pain patients revealed loss of power in the slow-5 frequency band (0.01 to 0.027 Hz) which developed only in the chronic phase of pain. This loss of power was reproducible across two cohorts of chronic low-back pain patients obtained from different sites and accurately classified chronic low-back pain patients in two additional independent datasets. Our results provide evidence that lower nucleus accumbens volume confers risk for developing chronic pain and altered nucleus accumbens activity is a signature of the state of chronic pain.

Schizophrenia is often associated with disrupted brain connectivity. However, identifying specific neuroimaging-based patterns pathognomonic for schizophrenia and related symptom severity remains a challenging open problem requiring large-scale datadriven analyses emphasizing not only statistical significance but also stability across multiple datasets, contexts and cohorts. Accurate prediction on previously unseen subjects, or generalization, is also essential for any useful biomarker of schizophrenia. In order to build a predictive model based on functional network feature patterns, we studied whole-brain fMRI functional networks, both at the voxel level and lower-resolution supervoxel level. Targeting Auditory Oddball task data on the FBIRN fMRI dataset (n = 95), we considered node-degree and link-weight network features and evaluated stability and generalization accuracy of statistically significant feature sets in discriminating patients vs. controls. We also applied sparse multivariate regression (elastic net) to whole-brain functional connectivity features, for the first time, to derive stable predictive features for symptom severity. Wholebrain link-weight features achieved 74% accuracy in identifying patients and were more stable than voxel-wise node-degrees. Linkweight features predicted severity of several negative and positive symptom scales, including inattentiveness and bizarre behavior. The most-significant, stable and discriminative functional connectivity changes involved increased correlations between thalamus and primary motor/primary sensory cortex, and between precuneus (BA7) and thalamus, putamen, and Brodmann areas BA9 and BA44. Precuneus, along with BA6 and primary sensory cortex, was also involved in predicting severity of several symptoms. Overall, the proposed multi-step methodology may help identify more reliable multivariate patterns allowing for accurate prediction of schizophrenia and its symptoms severity.

No abstract

Patient stratification is critical for the sensitivity of clinical trials at early stages of neurodegenerative disorders. in Huntington's disease (HD), genetic tests make cognitive, motor and brain imaging measurements possible before symptom manifestation (pre-HD). We evaluated pre-HD stratification models based on single visit resting-state functional MRi (rs-fMRi) data that assess observed longitudinal motor and cognitive change rates from the multisite Track-On HD cohort (74 pre-HD, 79 control participants). We computed longitudinal performance change on 10 tasks (including visits from the preceding tRAcK-HD study when available), as well as functional connectivity density (fcD) maps in single rs-fMRi visits, which showed high test-retest reliability. We assigned pre-HD subjects to subgroups of fast, intermediate, and slow change along single tasks or combinations of them, correcting for expectations based on aging; and trained FCD-based classifiers to distinguish fast-from slow-progressing individuals. For robustness, models were validated across imaging sites. Stratification models distinguished fast-from slow-changing participants and provided continuous assessments of decline applicable to the whole pre-HD population, relying on previously-neglected white matter functional signals. these results suggest novel correlates of early deterioration and a robust stratification strategy where a single MRI measurement provides an estimate of multiple ongoing longitudinal changes.