Search

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]

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.

This article presents a detailed description of an algorithm for the automatic detection of the mid-sagittal plane in three-dimensional (3-D) brain images. The algorithm seeks the plane with respect to which the image exhibits maximum symmetry. For a given plane, symmetry is measured by the cross-correlation between the image sections lying on either side. The search for the plane of maximum symmetry is performed by using a multiresolution approach which substantially decreases computational time. The choice of the starting plane was found to be an important issue in optimization. A method for selecting the initial plane is presented. The algorithm has been tested on brain images from various imaging modalities in both humans and animals. Results were evaluated by visual inspection by neuroradiologists and were judged to be consistently correct. Index Terms - Brain, image registration, medical imaging, mid-sagittal plane, pattern recognition.

Abstract Analyses were performed on brain MRI scans from individuals who were frequent cannabis users (N = 10; 9 males, 1 female, mean age 21.1 ± 2.9, range: 18–27) in adolescence and similar age and sex matched young adults who never used cannabis (N = 10; 9 males, 1 female, mean age of 23.0 ± 4.4, range: 17–30). Cerebral atrophy and white matter integrity were determined using diffusion tensor imaging (DTI) to quantify the apparent diffusion coefficient (ADC) and the fractional anisotropy (FA). Whole brain volumes, lateral ventricular volumes, and gray matter volumes of the amygdala-hippocampal complex, superior temporal gyrus, and entire temporal lobes (excluding the amygdala-hippocampal complex) were also measured. While differences existed between groups, no pattern consistent with evidence of cerebral atrophy or loss of white matter integrity was detected. It is concluded that frequent cannabis use is unlikely to be neurotoxic to the normal developing adolescent brain.

The methods of Bayesian statistics are applied to the analysis of fMRI data. Three specific models are examined. The first is the familiar linear model with white Gaussian noise. In this section, the Jeffreys' Rule for noninformative prior distributions is stated and it is shown how the posterior distribution may be used to infer activation in individual pixels. Next, linear time-invariant (LTI) systems are introduced as an example of statistical models with nonlinear parameters. It is shown that the Bayesian approach can lead to quite complex bimodal distributions of the parameters when the specific case of a delta function response with a spatially varying delay is analyzed. Finally, a linear model with auto-regressive noise is discussed as an alternative to that with uncorrelated white Gaussian noise. The analysis isolates those pixels that have significant temporal correlation under the model. It is shown that the number of pixels that have a significantly large auto-regression parameter is dependent on the terms used to account for confounding effects. Index Terms--Auto-regressive modeling, Bayesian statistics, functional MRI data analysis, linear time-invariant systems.

A statistical method for detecting activated pixels in functional MRI (fMRI) data is presented. In this method, the fMRI time series measured at each pixel is modeled as the sum of a response signal which arises due to the experimentally controlled activation-baseline pattern, a nuisance component representing effects of no interest, and Gaussian white noise. For periodic activation-baseline patterns, the response signal is modeled by a truncated Fourier series with a known fundamental frequency but unknown Fourier coefficients. The nuisance subspace is assumed to be unknown. A maximum likelihood estimate is derived for the component of the nuisance subspace which is orthogonal to the response signal subspace. An estimate for the order of the nuisance subspace is obtained from an information theoretic criterion. A statistical test is derived and shown to be the uniformly most powerful (UMP) test invariant to a group of transformations which are natural to the hypothesis testing problem. The maximal invariant statistic used in this test has an F distribution. The theoretical F distribution under the null hypothesis strongly concurred with the experimental frequency distribution obtained by performing null experiments in which the subjects did not perform any activation task. Application of the theory to motor activation and visual stimulation fMRI studies is presented. Index Terms - Brain, functional MRI, maximum likelihood estimation, statistical analysis.

The objective of this study was to investigate the clinical and neuropsychological correlates of white matter abnormalities in patients with schizophrenia studied early in the course of illness. A total of 33 (21 male/12 female) patients with recent onset schizophrenia and 30 (18 male/12 female) healthy volunteers completed structural and diffusion tensor imaging exams. Patients also received clinical and neuropsychological assessments. Fractional anisotropy (FA) maps were compared between groups in the white matter using a voxelwise analysis following intersubject registration to Talairach space and correlated with functional indices. Compared to healthy volunteers, patients demonstrated significantly (p<0.001, cluster size 100) lower FA within temporal lobe white matter regions corresponding approximately to the right and left uncinate fasciculus, left inferior fronto-occipital fasciculus, and left superior longitudinal fasciculus. There were no areas of significantly higher FA in patients compared to healthy volunteers. Lower FA in the bilateral uncinate fasciculus correlated significantly with greater severity of negative symptoms (alogia and affective flattening), and worse verbal learning/memory functioning. In addition, higher FA in the inferior fronto-occipital fasciculus correlated significantly with greater severity of delusions and hallucinations. White matter abnormalities are evident in patients with schizophrenia early in the course of illness, appearing most robust in left temporal regions. These abnormalities have clinical and neuropsychological correlates, which may be useful in further characterizing structure–function relations in schizophrenia and constraining neurobiological models of the disorder.Neuropsychopharmacology (2008) 33, 976–984; doi:10.1038/sj.npp.1301480; published online 20 June 2007 [ABSTRACT FROM AUTHOR]

Describes a algorithm developed for the automatic detection of the mid-sagittal plane (MSP) in three-dimensional (3-D) brain images. Indepth look at the implementation of the algorithm; Application of the method to a large number of images; Evaluation of the results.

Brain age measures predicted from structural and functional brain features are increasingly being used to understand brain integrity, disorders, and health. While there is a vast literature showing aberrations in both structural and functional brain measures in individuals with and at risk for alcohol use disorder (AUD), few studies have investigated brain age in these groups. The current study examines brain age measures predicted using brain morphological features, such as cortical thickness and brain volume, in individuals with a lifetime diagnosis of AUD as well as in those at higher risk to develop AUD from families with multiple members affected with AUD (i.e., higher family history density (FHD) scores). The AUD dataset included a group of 30 adult males (mean age = 41.25 years) with a lifetime diagnosis of AUD and currently abstinent and a group of 30 male controls (mean age = 27.24 years) without any history of AUD. A second dataset of young adults who were categorized based on their FHD scores comprised a group of 40 individuals (20 males) with high FHD of AUD (mean age = 25.33 years) and a group of 31 individuals (18 males) with low FHD (mean age = 25.47 years). Brain age was predicted using 187 brain morphological features of cortical thickness and brain volume in an XGBoost regression model; a bias-correction procedure was applied to the predicted brain age. Results showed that both AUD and high FHD individuals showed an increase of 1.70 and 0.09 years (1.08 months), respectively, in their brain age relative to their chronological age, suggesting accelerated brain aging in AUD and risk for AUD. Increased brain age was associated with poor performance on neurocognitive tests of executive functioning in both AUD and high FHD individuals, indicating that brain age can also serve as a proxy for cognitive functioning and brain health. These findings on brain aging in these groups may have important implications for the prevention and treatment of AUD and ensuing cognitive decline., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest.