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Patients with bipolar I disorder demonstrated white matter abnormalities in white matter regions as seen through the use of diffusion tensor imaging. The findings suggest that white matter abnormalities in pediatric bipolar disorder may be useful in constructing neurobiological models of the disorder.

Objectives Disengagement from treatment is common in first episode schizophrenia (FES) and is associated with poor outcomes. Our aim was to determine whether hippocampal subfield volumes predict disengagement during maintenance treatment of FES. Methods FES patients were recruited from sites in Boston, New York, Shanghai, and Changsha. After stabilization on antipsychotic medication, participants were randomized to add-on citalopram or placebo and followed for 12 months. Demographic, clinical and cognitive factors at baseline were compared between completers and disengagers in addition to volumes of hippocampal subfields. Results Baseline data were available for 95 randomized participants. Disengagers (n = 38, 40%) differed from completers (n = 57, 60%) by race (more likely Black; less likely Asian) and in more alcohol use, parkinsonism, negative symptoms and more impairment in visual learning and working memory. Bilateral dentate gyrus (DG), CA1, CA2/3 and whole hippocampal volumes were significantly smaller in disengagers compared to completers. When all the eight volumes were entered into the model simultaneously, only left DG volume significantly predicted disengagement status and remained significant after adjusting for age, sex, race, intracranial volume, antipsychotic dose, duration of untreated psychosis, citalopram status, alcohol status, and smoking status (P <.01). Left DG volume predicted disengagement with 57% sensitivity and 83% specificity. Conclusions Smaller left DG was significantly associated with disengagement status over 12 months of maintenance treatment in patients with FES participating in a randomized clinical trial. If replicated, these findings may provide a biomarker to identify patients at risk for disengagement and a potential target for interventions. [ABSTRACT FROM AUTHOR]

Background: Hippocampal atrophy is a diagnostic marker of AD and various compartments of the basal forebrain cholinergic (BFC) system also show volume reductions that correlate with cortical thinning and volume reduction in their innervated targets in the hippocampus. Moreover, bilateral volume loss of the nucleus basalis Meynert (Ch4) and spreading the atrophy to medial septum and vertical limb of the diagonal band of Broca (Ch12) in preclinical and prodromal AD show correlation with abnormal CSF levels of Aβ1‐42 and phosphorylated tau. It is unclear, however, how volume integrity of hippocampus and various compartments of the cholinergic space (Ch12 and Ch4) associate with specific cognitive domains. Method: Data from 402 participants were downloaded from the AD Neuroimaging Initiative [120 AD, 118 cognitively normal (CN), and 164 mild cognitive impairment (MCI)]. For each participant, domain‐specific performance scores were downloaded for: memory (ADNI‐MEM), executive function (ADNI‐EF), language (ADNI‐LAN) and visuo‐spatial ability (ADNI‐VS). In addition, all participants had 3D T1‐weighted structural MRI scans (3T) from which we computed the structural integrity measures for the left and right hippocampus (LHC, RHC), Ch4 (LCH4, RCH4) and Ch12 (LCH12, RCH12) BFC nuclei. Canonical correlation analysis (CCA) was used to determine the association between the 4 cognitive measures on the one hand and the 6 brain structural MRI measures on the other hand after controlling for age and sex. Result: The first and second canonical correlation coefficients were found to be statistically significant: r1 =.56 (p<.001) and r2 =.23 (p =.011). The first pair of canonical correlation vectors (CCVs), indicated that predominantly ADNI‐MEM (Fig. 1) is associate with a bilateral hippocampal integrity (LHC and RHC) (Fig. 2) particularly on the right. Moreover, the corresponding cognitive and structural canonical variates differentiated between CN, MCI and AD (Fig. 3). The second pair of CCVs indicated that predominately ADNI‐LAN (Fig. 4) was associated with RHC>LHC asymmetry (Fig. 5). However, the corresponding canonical variates did not differentiate between groups (Fig. 6). Conclusion: Bilateral hippocampal atrophy is associated with reduced ADNI‐MEM performance, and both differentiate CN, MCI and AD. Asymmetry of the hippocampal volumetric integrity is associated with ADNI‐LAN (LZ was supported by NIH RF1NS023945‐28). [ABSTRACT FROM AUTHOR]

An adaptive spatial filtering method is proposed that takes into account contextual information in fMRI activation detection. This filter replaces the time series of each voxel with a weighted average of time series of a small neighborhood around it. The filter coefficients at each voxel are derived so as to maximize a test statistic designed to indicate the presence of activation. This statistic is the ratio of the energy of the filtered time series in a signal subspace to the energy of the residuals. It is shown that the filter coefficients and the maximum energy ratio can be found through a generalized eigenproblem. This approach equates the filter coefficients to the elements of an eigenvector corresponding to the largest eigenvalue of a specific matrix, while the largest eigenvalue itself becomes the maximum energy ratio that can be used as a statistic for detecting activation. The distribution of this statistic under the null hypothesis is derived by a nonparametric permutation technique in the wavelet domain. Also, in this paper we introduce a new set of basis vectors that define the signal subspace. The space spanned by these basis vectors covers a wide range of possible hemodynamic response functions (HRF) and is applicable to both event related and block design fMRI signal analysis. This approach circumvents the need for a priori assumptions about the exact shape of the HRF. Resting-state experimental fMRI data were used to assess the specificity of the method, showing that the actual false-alarm rate of the proposed method is equal or less than its expected value. Analysis of simulated data and motor task fMRI datasets from six volunteers using the method proposed here showed an improved sensitivity as compared to a conventional test with a similar statistic applied to spatially smoothed data. Index Terms--Adaptive spatial filtering, fMRI activation detection, permutation, signal subspace.

A new method is proposed for activation detection in event-related functional magnetic resonance imaging (fMRI). The method is based on the analysis of selected resolution levels (a subspace) in translation invariant wavelet transform (TIWT) domain. Using a priori knowledge about the activation signal and trends, we analyze their power in different resolution levels in TIWT domain and select an optimal set of resolution levels. A randomization-based statistical test is then applied in the wavelet domain for activation detection. This approach suppresses the effects of trends and enhances the detection sensitivity. In addition, since TIWT is insensitive to signal translations, the power analysis is robust with respect to signal shifts. The randomization test alleviates the need for assumptions about fMRI noise. The method has been applied to simulated and experimental fMRI datasets. Comparisons have been made between the results of the proposed method, a similar method in the time domain and the cross-correlation method. The proposed method has shown superior sensitivity compared to the other methods. Index Terms--Functional magnetic resonance imaging (fMRI), randomization, signal detection, translation invariant wavelet transform.