Sensitivity of Different MRI-Techniques to Assess Gray Matter Atrophy Patterns in Alzheimer’s Disease is Region-Specific

Multimodal transformer network for incomplete image generation and diagnosis of Alzheimer's disease

Multimodal images such as magnetic resonance imaging (MRI) and positron emission tomography (PET) could provide complementary information about the brain and have been widely investigated for the diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD). However, multimodal brain images are often incomplete in clinical practice. It is still challenging to make use of multimodality for disease diagnosis with missing data. In this paper, we propose a deep learning framework with the multi-level guided generative adversarial network (MLG-GAN) and multimodal transformer (Mul-T) for incomplete image generation and disease classification, respectively. First, MLG-GAN is proposed to generate the missing data, guided by multi-level information from voxels, features, and tasks. In addition to voxel-level supervision and task-level constraint, a feature-level auto-regression branch is proposed to embed the features of target images for an accurate generation. With the complete multimodal images, we propose a Mul-T network for disease diagnosis, which can not only combine the global and local features but also model the latent interactions and correlations from one modality to another with the cross-modal attention mechanism. Comprehensive experiments on three independent datasets (i.e., ADNI-1, ADNI-2, and OASIS-3) show that the proposed method achieves superior performance in the tasks of image generation and disease diagnosis compared to state-of-the-art methods.

Brain Status Transferring Generative Adversarial Network for Decoding Individualized Atrophy in Alzheimer's Disease

Deep learning has been widely investigated in brain image computational analysis for diagnosing brain diseases such as Alzheimer's disease (AD). Most of the existing methods built end-to-end models to learn discriminative features by group-wise analysis. However, these methods cannot detect pathological changes in each subject, which is essential for the individualized interpretation of disease variances and precision medicine. In this article, we propose a brain status transferring generative adversarial network (BrainStatTrans-GAN) to generate corresponding healthy images of patients, which are further used to decode individualized brain atrophy. The BrainStatTrans-GAN consists of generator, discriminator, and status discriminator. First, a normative GAN is built to generate healthy brain images from normal controls. However, it cannot generate healthy images from diseased ones due to the lack of paired healthy and diseased images. To address this problem, a status discriminator with adversarial learning is designed in the training process to produce healthy brain images for patients. Then, the residual between the generated and input images can be computed to quantify pathological brain changes. Finally, a residual-based multi-level fusion network (RMFN) is built for more accurate disease diagnosis. Compared to the existing methods, our method can model individualized brain atrophy for facilitating disease diagnosis and interpretation. Experimental results on T1-weighted magnetic resonance imaging (MRI) data of 1,739 subjects from three datasets demonstrate the effectiveness of our method.

Concordance of Alzheimer’s Disease Subtypes Produced from Different Representative Morphological Measures: A Comparative Study

Background: Gray matter (GM) density and cortical thickness (CT) obtained from structural magnetic resonance imaging are representative GM morphological measures that have been commonly used in Alzheimer’s disease (AD) subtype research. However, how the two measures affect the definition of AD subtypes remains unclear. Methods: A total of 180 AD patients from the ADNI database were used to identify AD subgroups. The subtypes were identified via a data-driven strategy based on the density features and CT features, respectively. Then, the similarity between the two features in AD subtype definition was analyzed. Results: Four distinct subtypes were discovered by both density and CT features: diffuse atrophy AD, minimal atrophy AD (MAD), left temporal dominant atrophy AD (LTAD), and occipital sparing AD. The matched subtypes exhibited relatively high similarity in atrophy patterns and neuropsychological and neuropathological characteristics. They differed only in MAD and LTAD regarding the carrying of apolipoprotein E ε2. Conclusions: The results verified that different representative morphological GM measurement methods could produce similar AD subtypes. Meanwhile, the influences of apolipoprotein E genotype, asymmetric disease progression, and their interactions should be considered and included in the AD subtype definition. This study provides a valuable reference for selecting features in future studies of AD subtypes.

Structural Alteration of Medial Temporal Lobe Subfield in the Amnestic Mild Cognitive Impairment Stage of Alzheimer’s Disease

Objective. Volume reduction and structural abnormality is the most replicated finding in neuroimaging studies of Alzheimer’s disease (AD). Amnestic mild cognitive impairment (aMCI) is the early stage of AD development. Thus, it is necessary to investigate the link between atrophy of regions of interest (ROIs) in medial temporal lobe, the variation trend of ROI densities and volumes among patients with cognitive impairment, and the distribution characteristics of ROIs in the aMCI group, Alzheimer’s disease (AD) group, and normal control (NC) group. Methods. 30 patients with aMCI, 16 patients with AD, and 30 NC are recruited; magnetic resonance imaging (MRI) brain scans are conducted. Voxel-based morphometry was employed to conduct the quantitative measurement of gray matter densities of the hippocampus, amygdala, entorhinal cortex, and mammillary body (MB). FreeSurfer was utilized to automatically segment the hippocampus into 21 subregions and the amygdala into 9 subregions. Then, their subregion volumes and total volume were calculated. Finally, the ANOVA and multiple comparisons were performed on the above-mentioned data from these three groups. Results. AD had lower GM densities than MCI, and MCI had lower GM densities than NC, but not all of the differences were statistically significant. In the comparisons of AD-aMCI-NC, AD-aMCI, and AD-NC, the hippocampus, amygdala, and entorhinal cortex showed differences in the gray matter densities ( $p<0.05$ ); the differences of mammillary body densities were not significant in the random comparison between these three groups ( $p>0.05$ ). The hippocampus densities and volumes of the subjects from the aMCI group and the AD group were bilaterally symmetric. The gray matter densities of the right side of the entorhinal cortex inside each group and the hippocampus from the NC group were higher than those of the left side ( $p<0.05$ ), and the gray matter densities of the amygdala and mammillary body were bilaterally symmetric in the three groups ( $p>0.05$ ). There were no gender differences of four ROIs in the AD, aMCI, and NC groups ( $p>0.05$ ). The volume differences of the hippocampus presubiculum-body and parasubiculum manifest no statistical significance ( $p>0.05$ ) in the random comparison between these three groups. Volume differences of the left amygdala basal nucleus, the left lateral nucleus, the left cortical amygdala transitional area, the left paravamnion nucleus, and bilateral hippocampal amygdala transition area (HATA) had statistical differences only between the AD group and the NC group ( $p<0.05$ ). Conclusion. Structural defects of medial temporal lobe subfields were revealed in the aMCI and AD groups. Decreased gray matter densities of the hippocampus, entorhinal cortex, and amygdala could distinguish patients with early stage of AD between aMCI and NC. Volume decline of the hippocampus and amygdala subfields could only distinguish AD between NC.

Agreement Between Automated and Manual MRI Volumetry in Alzheimer's Disease: A Systematic Review and Meta-Analysis

BACKGROUND

Automated magnetic resonance imaging (MRI) volumetry is a promising tool to evaluate regional brain volumes in dementia and especially Alzheimer's disease (AD).

PURPOSE

To compare automated methods and the gold standard manual segmentation in measuring regional brain volumes on MRI across healthy controls, patients with mild cognitive impairment, and patients with dementia due to AD.

STUDY TYPE

Systematic review and meta-analysis.

DATA SOURCES

MEDLINE, Embase, and PsycINFO were searched through October 2021.

FIELD STRENGTH

1.0 T, 1.5 T, or 3.0 T.

ASSESSMENT

Two review authors independently identified studies for inclusion and extracted data. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2).

STATISTICAL TESTS

Standardized mean differences (SMD; Hedges' g) were pooled using random-effects meta-analysis with robust variance estimation. Subgroup analyses were undertaken to explore potential sources of heterogeneity. Sensitivity analyses were conducted to examine the impact of the within-study correlation between effect estimates on the meta-analysis results.

RESULTS

Seventeen studies provided sufficient data to evaluate the hippocampus, lateral ventricles, and parahippocampal gyrus. The pooled SMD for the hippocampus, lateral ventricles, and parahippocampal gyrus were 0.22 (95% CI -0.50 to 0.93), 0.12 (95% CI -0.13 to 0.37), and -0.48 (95% CI -1.37 to 0.41), respectively. For the hippocampal data, subgroup analyses suggested that the pooled SMD was invariant across clinical diagnosis and field strength. Subgroup analyses could not be conducted on the lateral ventricles data and the parahippocampal gyrus data due to insufficient data. The results were robust to the selected within-study correlation value.

DATA CONCLUSION

While automated methods are generally comparable to manual segmentation for measuring hippocampal, lateral ventricle, and parahippocampal gyrus volumes, wide 95% CIs and large heterogeneity suggest that there is substantial uncontrolled variance. Thus, automated methods may be used to measure these regions in patients with AD but should be used with caution.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 3.

A Bayesian group lasso classification for ADNI volumetrics data

The primary objective of this paper is to develop a statistically valid classification procedure for analyzing brain image volumetrics data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) in elderly subjects with cognitive impairments. The Bayesian group lasso method thereby proposed for logistic regression efficiently selects an optimal model with the use of a spike and slab type prior. This method selects groups of attributes of a brain subregion encouraged by the group lasso penalty. We conduct simulation studies for high- and low-dimensional scenarios where our method is always able to select the true parameters that are truly predictive among a large number of parameters. The method is then applied on dichotomous response ADNI data which selects predictive atrophied brain regions and classifies Alzheimer's disease patients from healthy controls. Our analysis is able to give an accuracy rate of 80% for classifying Alzheimer's disease. The suggested method selects 29 brain subregions. The medical literature indicates that all these regions are associated with Alzheimer's patients. The Bayesian method of model selection further helps selecting only the subregions that are statistically significant, thus obtaining an optimal model.

Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment

BACKGROUND

Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year.

OBJECTIVES

To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI.

SEARCH METHODS

On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches.

SELECTION CRITERIA

We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter.

DATA COLLECTION AND ANALYSIS

Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available.

MAIN RESULTS

We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants, but the manual MRI technique was superior to automatic techniques in mixed (mostly indirect) comparisons. We did not assess the relative accuracy of the volumes of different brain regions measured by MRI because only indirect comparisons were available, studies were heterogeneous, and the overall accuracy of all regions was moderate.

AUTHORS' CONCLUSIONS

The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.

Associations between cortical thickness and auditory verbal hallucinations in patients with schizophrenia: A systematic review

Auditory verbal hallucinations are common symptoms in schizophrenia patients, and recent magnetic resonance imaging studies have suggested associations between cortical thickness and auditory verbal hallucinations. This article summarises the associations between cortical thickness reduction and auditory verbal hallucinations, conceptualising the findings based on the Research Domain Criteria framework. Six studies identified in a systematic literature search were included in the review. Cortical thickness reductions in schizophrenia patients with auditory verbal hallucinations were reported in the transverse temporal gyrus in four of the studies, in the superior temporal gyrus in three of them, and in the middle temporal gyrus in three of the studies. These regions are collectively associated with auditory perception in the cognitive system domain in the Research Domain Criteria. Findings in other brain areas were inconsistent, which may reflect uncharacterised differences in the phenomenology and subjective experience of auditory verbal hallucinations. Future studies are encouraged to apply the Research Domain Criteria to characterise other putative networks associated with the subjective experience of auditory verbal hallucinations. This approach may facilitate understanding of current inconsistencies between auditory verbal hallucinations and cortical thickness in other brain areas.

An MRI evaluation of grey matter damage in African Americans with MS

OBJECTIVE

Multiple sclerosis (MS) is less prevalent in African Americans (AAs) than Caucasians (CAs) but in the former the disease course tends to be more severe. In order to clarify the MRI correlates of disease severity in AAs, we performed a multimodal brain MRI study to comprehensively assess the extent of grey matter (GM) damage and the degree of functional adaptation to structural damage in AAs with MS.

METHODS

In this cross-sectional study, we characterized GM damage in terms of focal lesions and volume loss and functional adaptation during the execution of a simple motor task on a sample of 20 AAs and 20 CAs with MS and 20 healthy controls (CTRLs).

RESULTS

In AAs, we observed a wider range of EDSS scores than CAs, with multisystem involvement being more likely in AAs (p < 0.01). While no significant differences were detected in lesion loads and global brain volumes, AAs showed regional atrophy in the posterior lobules of cerebellum, temporo-occipital and frontal regions in comparison with CAs (p < 0.01), with cerebellar atrophy being the best metric in differentiating AAs from CAs (p = 0.007, AUC = 0.96 and p = 0.005, AUC = 0.96, respectively for right and left cerebellar clusters). In AAs, the functional analysis of cortical activations showed an increase in task-related activation of areas involved in high level processing and a decreased activation in the medial prefrontal cortex compared to CAs.

INTERPRETATION

In our study, the direct comparison of AAs and CAs points to cerebellar atrophy as the main difference between subgroups.

Shades of white: diffusion properties of T1- and FLAIR-defined white matter signal abnormalities differ in stages from cognitively normal to dementia

The underlying pathology of white matter signal abnormalities (WMSAs) is heterogeneous and may vary dependent on the magnetic resonance imaging contrast used to define them. We investigated differences in white matter diffusivity as an indicator for white matter integrity underlying WMSA based on T1-weighted and fluid-attenuated inversion recovery (FLAIR) imaging contrast. In addition, we investigated which white matter region of interest (ROI) could predict clinical diagnosis best using diffusion metrics. One hundred three older individuals with varying cognitive impairment levels were included and underwent neuroimaging. Diffusion metrics were extracted from WMSA areas based on T1 and FLAIR contrast and from their overlapping areas, the border surrounding the WMSA and the normal-appearing white matter (NAWM). Regional diffusivity differences were calculated with linear mixed effects models. Multinomial logistic regression determined which ROI diffusion values classified individuals best into clinically defined diagnostic groups. T1-based WMSA showed lower white matter integrity compared to FLAIR WMSA-defined regions. Diffusion values of NAWM predicted diagnostic group best compared to other ROI's. To conclude, T1- or FLAIR-defined WMSA provides distinct information on the underlying white matter integrity associated with cognitive decline. Importantly, not the "diseased" but the NAWM is a potentially sensitive indicator for cognitive brain health status.

Insular atrophy at the prodromal stage of dementia with Lewy bodies: a VBM DARTEL study

Diffuse atrophy including the insula was previously demonstrated in dementia with Lewy bodies (DLB) patients but little is known about the prodromal stage of DLB (pro-DLB). In this prospective study, we used SPM8-DARTEL to measure gray matter (GM) and white matter (WM) atrophy in pro-DLB patients (n = 54), prodromal Alzheimer’s disease (pro-AD) patients (n = 16), DLB patients at the stage of dementia (mild-DLB) (n = 15), and Alzheimer’s disease patients at the stage of dementia (mild-AD) (n = 28), and compared them with healthy elderly controls (HC, n = 22). Diminished GM volumes were found in bilateral insula in pro-DLB patients, a trend to significance in right hippocampus and parahippocampal gyrus in pro-AD patients, in left insula in mild-DLB patients, and in medial temporal lobes and insula in mild-AD patients. The comparison between prodromal groups did not showed any differences. The comparison between groups with dementia revealed atrophy around the left middle temporal gyrus in mild-AD patients. Reduced WM volume was observed in mild-DLB in the pons. The insula seems to be a key region in DLB as early as the prodromal stage. MRI studies looking at perfusion, and functional and anatomical connectivity are now needed to better understand the role of this region in DLB.

Structural and Neuronal Integrity Measures of Fatigue Severity in Multiple Sclerosis

Fatigue is a common and disabling symptom in Multiple Sclerosis (MS). However, consistent neuroimaging correlates of its severity are not fully elucidated. In this article, we study the neuronal correlates of fatigue severity in MS. Forty-three Relapsing Remitting MS (RRMS) patients with MS-related fatigue (Fatigue Severity Scale (FSS) range: 1–7) and Expanded Disability Status Scale (EDSS) ≤ 4, were divided into high fatigue (HF, FSS ≥ 5.1) and low fatigue groups (LF, FSS ≤ 3). We measured T2 lesion load using a semi-automated technique. Cortical thickness, volume of sub-cortical nuclei, and brainstem structures were measured using Freesurfer. Cortical Diffusion Tensor Imaging (DTI) parameters were extracted using a cross modality technique. A correlation analysis was performed between FSS, volumetric, and DTI indices across all patients. HF patients showed significantly lower volume of thalamus, (p = 0.02), pallidum (p = 0.01), and superior cerebellar peduncle ((SCP), p = 0.002). The inverse correlation between the FSS score and the above volumes was significant in the total study population. In the right temporal cortex (RTC), the Radial Diffusivity ((RD), p = 0.01) and Fractional Anisotropy ((FA), p = 0.01) was significantly higher and lower, respectively, in the HF group. After Bonferroni correction, thalamic volume, FA-RTC, and RD-RTC remained statistically significant. Multivariate regression analysis identified FA-RTC as the best predictor of fatigue severity. Our data suggest an association between fatigue severity and volumetric changes of thalamus, pallidum, and SCP. Early neuronal injury in the RTC is implicated in the pathogenesis of MS-related fatigue.

The anteroposterior and primary-to-posterior limbic ratios as MRI-derived volumetric markers of Alzheimer's disease

BACKGROUND/AIMS

Alzheimer's disease (AD) shows a characteristic pattern of brain atrophy, with predominant involvement of posterior limbic structures, and relative preservation of rostral limbic and primary cortical regions. We aimed to investigate the diagnostic utility of two gray matter volume ratios based on this pattern, and to develop a fully automated method to calculate them from unprocessed MRI files.

PATIENTS AND METHODS

Cross-sectional study of 118 subjects from the ADNI database, including normal controls and patients with mild cognitive impairment (MCI) and AD. Clinical variables and 3T T1-weighted MRI files were analyzed. Regional gray matter and total intracranial volumes were calculated with a shell script (gm_extractor) based on FSL. Anteroposterior and primary-to-posterior limbic ratios (APL and PPL) were calculated from these values. Diagnostic utility of variables was tested in logistic regression models using Bayesian model averaging for variable selection. External validity was evaluated with bootstrap sampling and a test set of 60 subjects.

RESULTS

gm_extractor showed high test-retest reliability and high concurrent validity with FSL's FIRST. Volumetric measurements agreed with the expected anatomical pattern associated with AD. APL and PPL ratios were significantly different between groups, and were selected instead of hippocampal and entorhinal volumes to differentiate normal from MCI or cognitively impaired (MCI plus AD) subjects.

CONCLUSION

APL and PPL ratios may be useful components of models aimed to differentiate normal subjects from patients with MCI or AD. These values, and other gray matter volumes, may be reliably calculated with gm_extractor.

Hippocampal subregion volume changes associated with antipsychotic treatment in first-episode psychosis

BACKGROUND

Hippocampal dysfunction is considered central to many neurobiological models of schizophrenia, yet there are few longitudinal in vivo neuroimaging studies that have investigated the relationship between antipsychotic treatment and morphologic changes within specific hippocampal subregions among patients with psychosis.

METHOD

A total of 29 patients experiencing a first episode of psychosis with little or no prior antipsychotic exposure received structural neuroimaging examinations at illness onset and then following 12 weeks of treatment with either risperidone or aripiprazole in a double-blind randomized clinical trial. In addition, 29 healthy volunteers received structural neuroimaging examinations at baseline and 12-week time points. We manually delineated six hippocampal subregions [i.e. anterior cornu ammonis (CA) 1-3, posterior CA1-3, subiculum, dentate gyrus/CA4, entorhinal cortex, and fimbria] from 3T magnetic resonance images using an established method with high inter- and intra-rater reliability.

RESULTS

Following antipsychotic treatment patients demonstrated significant reductions in dentate gyrus/CA4 volume and increases in subiculum volume. Healthy volunteers demonstrated non-significant volumetric changes in these subregions across the two time points. We observed a significant quadratic (i.e. inverted U) association between changes in dentate gyrus/CA4 volume and cumulative antipsychotic dosage between the scans.

CONCLUSIONS

This study provides the first evidence to our knowledge regarding longitudinal in vivo volumetric changes within specific hippocampal subregions in patients with psychosis following antipsychotic treatment. The finding of a non-linear relationship between changes in dentate gyrus/CA4 subregion volume and antipsychotic exposure may provide new avenues into understanding dosing strategies for therapeutic interventions relevant to neurobiological models of hippocampal dysfunction in psychosis.

Neuro-degeneration profile of Alzheimer's patients: A brain morphometry study

INTRODUCTION

Alzheimer's disease (AD) is a primary and progressive neurodegenerative disorder, which is marked by cognitive deterioration and memory impairment. Atrophy of hippocampus and other basal brain regions is one of the most predominant structural imaging findings related to AD. Most studies have evaluated the pre-clinical and initial stages of AD through clinical trials using Magnetic Resonance Imaging. Structural biomarkers for advanced AD stages have not been evaluated yet, being considered only hypothetically.

OBJECTIVE

To evaluate the brain morphometry of AD patients at all disease stages, identifying the structural neuro-degeneration profile associated with AD severity.

MATERIAL AND METHODS

AD patients aged 60 years or over at different AD stages were recruited and grouped into three groups following the Clinical Dementia Rating (CDR) score: CDR1 (n = 16), CDR2 (n = 15), CDR3 (n = 13). Age paired healthy volunteers (n = 16) were also recruited (control group). Brain images were acquired on a 3T magnetic resonance scanner using a conventional Gradient eco 3D T1-w sequence without contrast injection. Volumetric quantitative data and cortical thickness were obtained by automatic segmentation using the Freesurfer software. Volume of each brain region was normalized by the whole brain volume in order to minimize age and body size effects. Volume and cortical thickness variations among groups were compared.

RESULTS

Atrophy was observed in the hippocampus, amygdala, entorhinal cortex, parahippocampal region, temporal pole and temporal lobe of patients suffering from AD at any stage. Cortical thickness was reduced only in the parahippocampal gyrus at all disease stages. Volume and cortical thickness were correlated with the Mini Mental State Examination (MMSE) score in all studied regions, as well as with CDR and disease duration.

DISCUSSION AND CONCLUSION

As previously reported, brain regions affected by AD during its initial stages, such as hippocampus, amygdala, entorhinal cortex, and parahippocampal region, were found to be altered even in individuals with severe AD. In addition, individuals, specifically, with CDR 3, have multiple regions with lower volumes than individuals with a CDR 2. These results indicate that rates of atrophy have not plateaued out at CDR 2-3, and in severe patients there are yet neuronal loss and gliosis. These findings can add important information to the more accepted model in the literature that focuses mainly on early stages. Our findings allow a better understanding on the AD pathophysiologic process and follow-up process of drug treatment even at advanced disease stages.

Grey matter atrophy in prodromal stage of dementia with Lewy bodies and Alzheimer's disease

BACKGROUND

Little is known about the patterns of brain atrophy in prodromal dementia with Lewy bodies (pro-DLB).

METHODS

In this study, we used SPM8 with diffeomorphic anatomical registration through exponentiated lie algebra to measure grey matter (GM) volume and investigate patterns of GM atrophy in pro-DLB (n = 28) and prodromal Alzheimer's disease (pro-AD) (n = 27) and compared and contrasted them with those in elderly control subjects (n = 33) (P ≤ 0.05 corrected for family-wise error).

RESULTS

Patients with pro-DLB showed diminished GM volumes of bilateral insulae and right anterior cingulate cortex compared with control subjects. Comparison of GM volume between patients with pro-AD and control subjects showed a more extensive pattern, with volume reductions in temporal (hippocampi and superior and middle gyri), parietal and frontal structures in the former. Direct comparison of prodromal groups suggested that more atrophy was evident in the parietal lobes of patients with pro-AD than patients with pro-DLB. In patients with pro-DLB, we found that visual hallucinations were associated with relative atrophy of the left cuneus.

CONCLUSIONS

Atrophy in pro-DLB involves the insulae and anterior cingulate cortex, regions rich in von Economo neurons, which we speculate may contribute to the early clinical phenotype of pro-DLB.

Enlargement of choroid plexus in complex regional pain syndrome

The choroid plexus, located in brain ventricles, has received surprisingly little attention in clinical neuroscience. In morphometric brain analysis, we serendipitously found a 21% increase in choroid plexus volume in 12 patients suffering from complex regional pain syndrome (CRPS) compared with age- and gender-matched healthy subjects. No enlargement was observed in a group of 8 patients suffering from chronic pain of other etiologies. Our findings suggest involvement of the choroid plexus in the pathogenesis of CRPS. Since the choroid plexus can mediate interaction between peripheral and brain inflammation, our findings pinpoint the choroid plexus as an important target for future research of central pain mechanisms.

Cortical Thickness in Dementia with Lewy Bodies and Alzheimer's Disease: A Comparison of Prodromal and Dementia Stages

Objectives

To assess and compare cortical thickness (CTh) of patients with prodromal Dementia with Lewy bodies (pro-DLB), prodromal Alzheimer's disease (pro-AD), DLB dementia (DLB-d), AD dementia (AD-d) and normal ageing.

Methods

Study participants(28 pro-DLB, 27 pro-AD, 31 DLB-d, 54 AD-d and 33 elderly controls) underwent 3Tesla T1 3D MRI and detailed clinical and cognitive assessments. We used FreeSurfer analysis package to measure CTh and investigate patterns of cortical thinning across groups.

Results

Comparison of CTh between pro-DLB and pro-AD (p<0.05, FDR corrected) showed more right anterior insula thinning in pro-DLB, and more bilateral parietal lobe and left parahippocampal gyri thinning in pro-AD. Comparison of prodromal patients to healthy elderly controls showed the involvement of the same regions. In DLB-d (p<0.05, FDR corrected) cortical thinning was found predominantly in the right temporo-parietal junction, and insula, cingulate, orbitofrontal and lateral occipital cortices. In AD-d(p<0.05, FDR corrected),the most significant areas affected included the entorhinal cortices, parahippocampal gyri and parietal lobes. The comparison of AD-d and DLB-d demonstrated more CTh in AD-d in the left entorhinal cortex (p<0.05, FDR corrected).

Conclusion

Cortical thickness is a sensitive measure for characterising patterns of grey matter atrophy in early stages of DLB distinct from AD. Right anterior insula involvement may be a key region at the prodromal stage of DLB and needs further investigation.

Corpus callosum atrophy associated with the degree of cognitive decline in patients with Alzheimer's dementia or mild cognitive impairment: a meta-analysis of the region of interest structural imaging studies

Individual structural neuroimaging studies of the corpus callosum (CC) in Alzheimer's disease (AD) and mild cognitive impairment (MCI) with the region of interest (ROI) analysis have yielded inconsistent findings. The aim of this study was to conduct a meta-analysis of structural imaging studies using ROI technique to measure the CC midsagittal area changes in patients with AD or MCI. Databases of PubMed, the Cochrane Library, the ISI Web of Science, and Science Direct from inception to June 2014 were searched with key words "corpus callosum" or "callosal", plus "Alzheimer's disease" or "mild cognitive impairment". Twenty-three studies with 603 patients with AD, 146 with MCI, and 638 healthy controls were included in this meta-analysis. Effect size was used to measure the difference between patients with AD or MCI and healthy controls. Significant callosal atrophy was found in MCI patients with an effect size of -0.36 (95% CI, -0.57 to -0.14; P = 0.001). The degree of the CC atrophy in mild AD was less severe than that in moderate AD with a mean effect size -0.69 (95% CI, -0.89 to -0.49) versus -0.92 (95% CI, -1.16 to -0.69), respectively. Comparing with healthy controls, patients with MCI had atrophy in the anterior portion of the CC (i.e., rostrum and genu). In contrast, patients with AD had atrophy in both anterior and posterior portions (i.e., splenium). These results suggest that callosal atrophy may be related to the degree of cognitive decline in patients with MCI and AD, and it may be used as a biomarker for patients with cognitive deficit even before meeting the criteria for AD.