Grey-matter texture abnormalities and reduced hippocampal volume are distinguishing features of schizophrenia

Neurodevelopmental processes are widely believed to underlie schizophrenia. Analysis of brain texture from conventional magnetic resonance imaging (MRI) can detect disturbance in brain cytoarchitecture. We tested the hypothesis that patients with schizophrenia manifest quantitative differences in brain texture that, alongside discrete volumetric changes, may serve as an endophenotypic biomarker. Texture analysis (TA) of grey matter distribution and voxel-based morphometry (VBM) of regional brain volumes were applied to MRI scans of 27 patients with schizophrenia and 24 controls. Texture parameters (uniformity and entropy) were also used as covariates in VBM analyses to test for correspondence with regional brain volume. Linear discriminant analysis tested if texture and volumetric data predicted diagnostic group membership (schizophrenia or control). We found that uniformity and entropy of grey matter differed significantly between individuals with schizophrenia and controls at the fine spatial scale (filter width below 2 mm). Within the schizophrenia group, these texture parameters correlated with volumes of the left hippocampus, right amygdala and cerebellum. The best predictor of diagnostic group membership was the combination of fine texture heterogeneity and left hippocampal size. This study highlights the presence of distributed grey-matter abnormalities in schizophrenia, and their relation to focal structural abnormality of the hippocampus. The conjunction of these features has potential as a neuroimaging endophenotype of schizophrenia

A Fair Trial: When the Constitution Requires Attorneys to Investigate Their Clients\u27 Brains

The U.S. Constitution guarantees every criminal defendant the right to a fair trial. This fundamental right includes the right to a defense counsel who provides effective assistance. To be effective, attorneys must sometimes develop specific types of evidence in crafting the best defense. In recent years, the U.S. Supreme Court has found that defense attorneys did not provide effective assistance when they failed to consider neuroscience. But when must defense attorneys develop neuroscience in order to provide effective assistance? This question is difficult because the standard for determining effective assistance is still evolving. There are two leading approaches. First, in Strickland v. Washington, the Court adopted a two-prong “reasonableness” test, which, according to Justice O’Conner, may result in court decisions that fail to properly protect a criminal defendant’s rights. Recently, courts have adopted a second approach based on guidelines promulgated by the American Bar Association. This Note aims to answer this question. It first provides a background on the right to effective assistance of counsel and briefly describes neuroscience evidence, oppositions to and limitations on in its use, and its admissibility in court. Second, this Note attempts to give some guidance to attorneys by exploring the American Bar Association and U.S. Supreme Court standards. Third, it summarizes the results of a statistical analysis conducted by the author, which helps further define when courts require attorneys to develop neuroscience evidence. It concludes by arguing that attorneys need guidance to ensure they are not violating the Sixth Amendment. This Note expands on the American Bar Association’s standard and suggests a framework attorneys may use to determine whether they should develop neuroscience evidence to ensure that their client has a fair trial

A Fair Trial: When the Constitution Requires Attorneys to Investigate Their Clients\u27 Brains

The U.S. Constitution guarantees every criminal defendant the right to a fair trial. This fundamental right includes the right to a defense counsel who provides effective assistance. To be effective, attorneys must sometimes develop specific types of evidence in crafting the best defense. In recent years, the U.S. Supreme Court has found that defense attorneys did not provide effective assistance when they failed to consider neuroscience. But when must defense attorneys develop neuroscience in order to provide effective assistance? This question is difficult because the standard for determining effective assistance is still evolving. There are two leading approaches. First, in Strickland v. Washington, the Court adopted a two-prong “reasonableness” test, which, according to Justice O’Conner, may result in court decisions that fail to properly protect a criminal defendant’s rights. Recently, courts have adopted a second approach based on guidelines promulgated by the American Bar Association. This Note aims to answer this question. It first provides a background on the right to effective assistance of counsel and briefly describes neuroscience evidence, oppositions to and limitations on in its use, and its admissibility in court. Second, this Note attempts to give some guidance to attorneys by exploring the American Bar Association and U.S. Supreme Court standards. Third, it summarizes the results of a statistical analysis conducted by the author, which helps further define when courts require attorneys to develop neuroscience evidence. It concludes by arguing that attorneys need guidance to ensure they are not violating the Sixth Amendment. This Note expands on the American Bar Association’s standard and suggests a framework attorneys may use to determine whether they should develop neuroscience evidence to ensure that their client has a fair trial

Association between a longer duration of illness, age and lower frontal lobe grey matter volume in schizophrenia

The frontal lobe has an extended maturation period and may be vulnerable to the long-term effects of schizophrenia. We tested this hypothesis by studying the relationship between duration of illness (DoI), grey matter (GM) and cerebro-spinal fluid (CSF) volume across the whole brain. Sixty-four patients with schizophrenia and 25 healthy controls underwent structural MRI scanning and neuropsychological assessment. We performed regression analyses in patients to examine the relationship between DoI and GM and CSF volumes across the whole brain, and correlations in controls between age and GM or CSF volume of the regions where GM or CSF volumes were associated with DoI in patients. Correlations were also performed between GM volume in the regions associated with DoI and neuropsychological performance. A longer DoI was associated with lower GM volume in the left dorsomedial prefrontal cortex (PFC), right middle frontal cortex, left fusiform gyrus (FG) and left cerebellum (lobule III). Additionally, age was inversely associated with GM volume in the left dorsomedial PFC in patients, and in the left FG and CSF excess near the left cerebellum in healthy controls. Greater GM volume in the left dorsomedial PFC was associated with better working memory, attention and psychomotor speed in patients. Our findings suggest that the right middle frontal cortex is particularly vulnerable to the long-term effect of schizophrenia illness whereas the dorsomedial PFC, FG and cerebellum are affected by both a long DoI and aging. The effect of illness chronicity on GM volume in the left dorsomedial PFC may be extended to brain structure–neuropsychological function relationships

Assessing neural tuning for object perception in schizophrenia and bipolar disorder with multivariate pattern analysis of fMRI data.

IntroductionDeficits in visual perception are well-established in schizophrenia and are linked to abnormal activity in the lateral occipital complex (LOC). Related deficits may exist in bipolar disorder. LOC contains neurons tuned to object features. It is unknown whether neural tuning in LOC or other visual areas is abnormal in patients, contributing to abnormal perception during visual tasks. This study used multivariate pattern analysis (MVPA) to investigate perceptual tuning for objects in schizophrenia and bipolar disorder.MethodsFifty schizophrenia participants, 51 bipolar disorder participants, and 47 matched healthy controls completed five functional magnetic resonance imaging (fMRI) runs of a perceptual task in which they viewed pictures of four different objects and an outdoor scene. We performed classification analyses designed to assess the distinctiveness of activity corresponding to perception of each stimulus in LOC (a functionally localized region of interest). We also performed similar classification analyses throughout the brain using a searchlight technique. We compared classification accuracy and patterns of classification errors across groups.ResultsStimulus classification accuracy was significantly above chance in all groups in LOC and throughout visual cortex. Classification errors were mostly within-category confusions (e.g., misclassifying one chair as another chair). There were no group differences in classification accuracy or patterns of confusion.ConclusionsThe results show for the first time MVPA can be used successfully to classify individual perceptual stimuli in schizophrenia and bipolar disorder. However, the results do not provide evidence of abnormal neural tuning in schizophrenia and bipolar disorder

Hepatic encephalopathy: a neurochemical, neuroanatomical, and neuropsychological study.

Hepatic encephalopathy (HE) is normally diagnosed by neuropsychological (NP) tests, which are not very specific and do not reveal the underlying pathology. Magnetic resonance imaging (MRI) and spectroscopy (MRS) of the brain offer alternative and possibly more specific markers for HE. These methods were applied in conjunction with NP testing in order to determine their usefulness in the identification of HE and to understand the pathogenesis of HE more clearly. MR imaging and spectroscopy examinations, in addition to a battery of 15 NP tests, were administered to investigate 31 patients awaiting liver transplantation and 23 healthy controls. MR image intensities from the globus pallidus region were calculated and normalized to those of the thalamus. Absolute concentrations and ratios with respect to creatine (Cr) of several metabolites were computed from MR spectra. The MR data were correlated with the results of NP tests. The patients showed impairment in NP tests of attention and visuospatial and verbal fluency. In T1-weighted MRI, the relative intensity of the globus pallidus with respect to that of the thalamus region was significantly elevated in patients and correlated(negatively) with three NP tests (Hooper, FAS, and Trails B). The absolute concentrations of myo-inositol (mI) and choline (Ch) were significantly reduced in three brain regions. In addition, the absolute concentrations of glutamine (Gln) and combined glutamate and glutamine (Glx) were increased in all three locations, with Gln increase being significant in all areas while that of Glx only in the occipital white matter. In summary, this study partially confirms a hypothesized mechanism of HE pathogenesis, an increased synthesis of glutamine by brain glutamate in astrocytes due to excessive blood ammonia, followed by a compensatory loss of myo-inositol to maintain astrocyte volume homeostasis. It also indicates that the hyperintensity observed in globus pallidus could be used as complementary to the NP test scores in evaluating the mental health of HE patients

DETERMINING EFFECTIVE LEVEL OF DEMENTIA DISEASE USING MRI IMAGES

Abstract The prevalence of dementia is growing as the world's population ages, making it a major public health issue. The key to successful management and treatment of dementia is an early and precise diagnosis. In this work, we will investigate the Dementia detection model DenseNet-169 in depth. The DenseNet-169 model has been used to classify almost 7,000 magnetic resonance imaging (MRI) scans of the brain. Non-Dementia, Mild Dementia, Severe Dementia, and Moderate Dementia are all categorized using this Convolution Neural Network (CNN) model. The use of deep learning and image processing presents intriguing new directions for the diagnosis and treatment of dementia, with the ultimate goal of enhancing the quality of life for those with the disease

An Intelligent Hybrid Optimization with Deep Learning model-based Schizophrenia Identification from Structural MRI

One of the fatal diseases that claim women while they are pregnant or nursing is schizophrenia. Despite several developments and symptoms, it can be challenging to discern between benign and malignant conditions. The main and most popular imaging method to predict Schizophrenia is MR Images. Furthermore, a few earlier models had a definite accuracy when diagnosing the condition. Stable MRI criteria must also be implemented immediately. Compared to other imaging technologies, the MRI imaging method is the simplest, safest, and most common for predicting Schizophrenia. The following factors are mostly involved in the subprocess for the initial MRI image. Before calculating the length between the sample point and the cluster center, the initial cluster center of the sample is identified. Classification is done according to how far the sample point is from the cluster center. The picture is then generated once the new cluster center has been derived using the classification history and verified to match the cluster convergence condition. A grey wolf optimization-based convolutional neural network approach is offered to get beyond the limitations and find schizophrenia, whether its hazardous or not. Many MRI images or datasets are analyzed in a short time, and the results show a more accurate or higher rate of schizophrenia recognition

Neural indicators of fatigue in chronic diseases : A systematic review of MRI studies

The authors would like to thank the Sir Jules Thorn Charitable Trust for their financial support.Peer reviewedPublisher PD