Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates.

OBJECTIVES: Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome. METHODS: In total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months. RESULTS: Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age. CONCLUSIONS: Brain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome. CLINICAL RELEVANCE STATEMENT: Understanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population. KEY POINTS: •Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes. •Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes. •The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates.

White matter hyperintensities segmentation using the ensemble U-Net with multi-scale highlighting foregrounds.

White matter hyperintensities (WMHs) are abnormal signals within the white matter region on the human brain MRI and have been associated with aging processes, cognitive decline, and dementia. In the current study, we proposed a U-Net with multi-scale highlighting foregrounds (HF) for WMHs segmentation. Our method, U-Net with HF, is designed to improve the detection of the WMH voxels with partial volume effects. We evaluated the segmentation performance of the proposed approach using the Challenge training dataset. Then we assessed the clinical utility of the WMH volumes that were automatically computed using our method and the Alzheimer's Disease Neuroimaging Initiative database. We demonstrated that the U-Net with HF significantly improved the detection of the WMH voxels at the boundary of the WMHs or in small WMH clusters quantitatively and qualitatively. Up to date, the proposed method has achieved the best overall evaluation scores, the highest dice similarity index, and the best F1-score among 39 methods submitted on the WMH Segmentation Challenge that was initially hosted by MICCAI 2017 and is continuously accepting new challengers. The evaluation of the clinical utility showed that the WMH volume that was automatically computed using U-Net with HF was significantly associated with cognitive performance and improves the classification between cognitive normal and Alzheimer's disease subjects and between patients with mild cognitive impairment and those with Alzheimer's disease. The implementation of our proposed method is publicly available using Dockerhub (https://hub.docker.com/r/wmhchallenge/pgs).

Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions.

Head motion during MRI acquisition presents significant challenges for neuroimaging analyses. In this work, we present a retrospective motion correction framework built on a Fourier domain motion simulation model combined with established 3D convolutional neural network (CNN) architectures. Quantitative evaluation metrics were used to validate the method on three separate multi-site datasets. The 3D CNN was trained using motion-free images that were corrupted using simulated artifacts. CNN based correction successfully diminished the severity of artifacts on real motion affected data on a separate test dataset as measured by significant improvements in image quality metrics compared to a minimal motion reference image. On the test set of 13 image pairs, the mean peak signal-to-noise-ratio was improved from 31.7 to 33.3 dB. Furthermore, improvements in cortical surface reconstruction quality were demonstrated using a blinded manual quality assessment on the Parkinson's Progression Markers Initiative (PPMI) dataset. Upon applying the correction algorithm, out of a total of 617 images, the number of quality control failures was reduced from 61 to 38. On this same dataset, we investigated whether motion correction resulted in a more statistically significant relationship between cortical thickness and Parkinson's disease. Before correction, significant cortical thinning was found to be restricted to limited regions within the temporal and frontal lobes. After correction, there was found to be more widespread and significant cortical thinning bilaterally across the temporal lobes and frontal cortex. Our results highlight the utility of image domain motion correction for use in studies with a high prevalence of motion artifacts, such as studies of movement disorders as well as infant and pediatric subjects.

Carbon monofilament electrodes for unit recording and functional MRI in same subjects.

Extracellular electrophysiology and functional MRI are complementary techniques that provide information about cellular and network-level neural activity, respectively. However, electrodes for electrophysiology are typically made from metals, which cause significant susceptibility artifacts on MR images. Previous work has demonstrated that insulated carbon fiber bundle electrodes reduce the volume of magnetic susceptibility artifacts and can be used to record local field potentials (LFP), but the relatively large diameter of the probes make them unsuitable for multi- and single-unit recordings. Although single carbon fiber electrodes have recently been used to record single-unit activity, these probes require modifications in order to aid insertion and the use of these probes in fMRI has yet to be validated. Therefore, there is a need for a single-carbon fiber electrode design that (1) minimizes the volume of the susceptibility artifact, (2) can record from a wide frequency band that includes LFP and multi- and single-unit recording, and (3) is practical to insert without additional modifications. Here, we demonstrate that carbon-fiber electrodes made from single carbon monofilaments (35 µm in diameter) meet all of these criteria. Carbon monofilament electrodes modified with the conductive polymer poly(3,4-ethylenedioxythiophene) (PEDOT) have lower impedances and higher signal-to-noise ratio recordings than platinum-iridium electrodes, a current gold standard for chronic single-unit recording. Furthermore, these probes distort a significantly smaller volume of voxels compared to tungsten and platinum-iridium electrodes in agarose phantom and in vivo MR images, leading to higher contrast-to-noise ratio in regions proximal to the electrode implantation site during fMRI. Collectively, this work establishes that carbon monofilaments are a practical choice for combined electrophysiology-fMRI experiments.

Comparison of fMRI analysis methods for heterogeneous BOLD responses in block design studies.

A large number of fMRI studies have shown that the temporal dynamics of evoked BOLD responses can be highly heterogeneous. Failing to model heterogeneous responses in statistical analysis can lead to significant errors in signal detection and characterization and alter the neurobiological interpretation. However, to date it is not clear that, out of a large number of options, which methods are robust against variability in the temporal dynamics of BOLD responses in block-design studies. Here, we used rodent optogenetic fMRI data with heterogeneous BOLD responses and simulations guided by experimental data as a means to investigate different analysis methods' performance against heterogeneous BOLD responses. Evaluations are carried out within the general linear model (GLM) framework and consist of standard basis sets as well as independent component analysis (ICA). Analyses show that, in the presence of heterogeneous BOLD responses, conventionally used GLM with a canonical basis set leads to considerable errors in the detection and characterization of BOLD responses. Our results suggest that the 3rd and 4th order gamma basis sets, the 7th to 9th order finite impulse response (FIR) basis sets, the 5th to 9th order B-spline basis sets, and the 2nd to 5th order Fourier basis sets are optimal for good balance between detection and characterization, while the 1st order Fourier basis set (coherence analysis) used in our earlier studies show good detection capability. ICA has mostly good detection and characterization capabilities, but detects a large volume of spurious activation with the control fMRI data.

Optogenetic study of networks in epilepsy.

Currently, approximately 30% of patients with epilepsy do not have adequate seizure control. A greater understanding of the underlying mechanisms by which seizures start or propagate could lead to new therapeutic strategies. The recent development of optogenetics, because of its unprecedented precision for controlling activity within distinct neuronal populations, has revolutionized neuroscience, including epilepsy research. This Review discusses recent breakthroughs made with optogenetics in epilepsy research. These breakthroughs include new insights into the key roles that different cell types play in mediating seizures as well as in the development of epilepsy. Subsequently, we discuss how targeting different brain regions and cell populations has opened up the possibility of highly specific therapies that can stop seizures on demand. Finally, we illustrate how combining newly available neuroscience tools with whole-brain imaging techniques will allow researchers to understand better the spread of seizures on a network level. © 2016 The Authors. Journal of Neuroscience Research Published by Wiley Periodicals, Inc.

MRI compatible optrodes for simultaneous LFP and optogenetic fMRI investigation of seizure-like afterdischarges.

In preclinical studies, implanted electrodes can cause severe degradation of MRI images and hence are seldom used for chronic studies employing functional magnetic resonance imaging. In this study, we developed carbon fiber optrodes (optical fiber and electrode hybrid devices), which can be utilised in chronic longitudinal studies aiming to take advantage of emerging optogenetic technologies, and compared them with the more widely used tungsten optrodes. We find that optrodes constructed using small diameter (~130 µm) carbon fiber electrodes cause significantly reduced artifact on functional MRI images compared to those made with 50 µm diameter tungsten wire and at the same time the carbon electrodes have lower impedance, which leads to higher quality LFP recordings. In order to validate this approach, we use these devices to study optogenetically-induced seizure-like afterdischarges in rats sedated with dexmedetomidine and compare these to sub (seizure) threshold stimulations in the same animals. The results indicate that seizure-like afterdischarges involve several extrahippocampal brain regions that are not recruited by subthreshold optogenetic stimulation of the hippocampus at 20 Hz. Subthreshold stimulation led to activation of the entire ipsilateral hippocampus and septum, whereas afterdischarges additionally produced activations in the contralateral hippocampal formation, neocortex, cerebellum, nucleus accumbens, and thalamus. Although we demonstrate just one application, given the ease of fabrication, we anticipate that carbon fiber optrodes could be utilised in a variety of studies that could benefit from longitudinal optogenetic functional magnetic resonance imaging.

Structural abnormality of the hippocampus associated with depressive symptoms in heart failure rats.

Heart failure (HF) is characterized by a blood supply which is insufficient to meet the body's demand. HF can potentially affect the brain and is associated with a high prevalence of depression. However, the mechanisms by which the two are related remain largely unclear. Structural abnormalities of the ventral hippocampus have been observed in depression but have never been reported in HF. In this study, we thus investigated structural brain abnormality in HF using voxel-based morphometry (VBM) and histological analysis in a rat model of HF. T2-weighted images were obtained in rats with HF (n = 20) and sham rats (n = 17) and VBM was used to produce gray matter concentration (GMC) maps. Twenty-four hour locomotor activity was used as a sign of depressive behavior. Brains of HF and sham rats (n = 8, each) were fixed and histologically analyzed for the measurement of neurogenesis, the number of astrocytes and neurite outgrowth in the ventral hippocampus. VBM demonstrated significant GMC decrease in the hippocampus, which was restricted to the ventral segment. Similarly, neurogenesis and neurite outgrowth were significantly decreased and the number of astrocytes was significantly increased in HF rats as compared with sham rats in the ventral hippocampus. GMC values in the ventral hippocampus were significantly and negatively correlated with 24 hour locomotor activity in HF rats. In conclusion, the present study has demonstrated for the first time that the structural abnormality of the ventral hippocampus is associated with depressive symptoms in HF rats.

Imaging seizure-induced inflammation using an antibody targeted iron oxide contrast agent.

Early inflammation following status epilepticus has been implicated in the development of epilepsy and the evolution of brain injury, yet its precise role remains unclear. The development of non-invasive imaging markers of inflammation would enable researchers to test this hypothesis in vivo and study its temporal progression in relation to epileptogenic insults. In this study we have investigated the potential of a targeted magnetic resonance imaging contrast agent--vascular cell adhesion molecule 1 antibody labelled iron oxide--to image the inflammatory process following status epilepticus in the rat lithium-pilocarpine model. Intravascular administration of the targeted contrast agent was performed at approximately 1 day following status epilepticus. The control group received diazepam prior to pilocarpine to prevent status epilepticus. Magnetic resonance imaging of rats was performed before and after contrast administration. Comparison with quantitative T2 measurements was also performed. At the end of the study, brains were removed for ex vivo magnetic resonance imaging and histology. Marked focal hypointensities caused by contrast agent binding were observed on in vivo magnetic resonance images in the post status epilepticus group. In particular these occurred in the periventricular organs, the hippocampus and the cerebral cortex. Relatively little contrast agent binding was observed in the control group. T2 relaxation times were not significantly increased for the hippocampus or the cerebral cortex in post status epilepticus animals. These results demonstrate the feasibility of in vivo imaging of seizure-induced inflammation in an animal model of epilepsy. The antibody targeted MRI contrast agent identified regions of acute inflammation following status epilepticus and may provide an early marker of brain injury. This technique could be used to determine the role of inflammation in models of epileptogenesis and to study the potential for anti-inflammatory therapeutic interventions.

Repeated hippocampal seizures lead to brain-wide reorganization of circuits and seizure propagation pathways.

Repeated seizure activity can lead to long-term changes in seizure dynamics and behavior. However, resulting changes in brain-wide dynamics remain poorly understood. This is due partly to technical challenges in precise seizure control and in vivo whole-brain mapping of circuit dynamics. Here, we developed an optogenetic kindling model through repeated stimulation of ventral hippocampal CaMKII neurons in adult rats. We then combined fMRI with electrophysiology to track brain-wide circuit dynamics resulting from non-afterdischarge (AD)-generating stimulations and individual convulsive seizures. Kindling induced widespread increases in non-AD-generating stimulation response and ipsilateral functional connectivity and elevated anxiety. Individual seizures in kindled animals showed more significant increases in brain-wide activity and bilateral functional connectivity. Onset time quantification provided evidence for kindled seizure propagation from the ipsilateral to the contralateral hemisphere. Furthermore, a core of slow-migrating hippocampal activity was identified in both non-kindled and kindled seizures, revealing a novel mechanism of seizure sustainment and propagation.

LEARNING TO SYNTHESIZE CORTICAL MORPHOLOGICAL CHANGES USING GRAPH CONDITIONAL VARIATIONAL AUTOENCODER.

Changes in brain morphology, such as cortical thinning are of great value for understanding the trajectory of brain aging and various neurodegenerative diseases. In this work, we employed a generative neural network variational autoencoder (VAE) that is conditional on age and is able to generate cortical thickness maps at various ages given an input cortical thickness map. To take into account the mesh topology in the model, we proposed a loss function based on weighted adjacency to integrate the surface topography defined as edge connections with the cortical thickness mapped as vertices. Compared to traditional conditional VAE that did not use the surface topological information, our method better predicted "future" cortical thickness maps, especially when the age gap became wider. Our model has the potential to predict the distinctive temporospatial pattern of individual cortical morphology in relation to aging and neurodegenerative diseases.

Improving brain age estimates with deep learning leads to identification of novel genetic factors associated with brain aging.

To study genetic factors associated with brain aging, we first need to quantify brain aging. Statistical models have been created for estimating the apparent age of the brain, or predicted brain age (PBA), using imaging data. Recent studies have refined these models to obtain a more accurate PBA, but research has yet to demonstrate the scientific value of doing so. Here, we show that a more accurate PBA leads to better characterization of genetic factors associated with brain aging. We trained a convolutional neural network (CNN) model on 16,998 UK Biobank subjects to derive PBA, then conducted a genome-wide association study on the PBA, in which we identified single nucleotide polymorphisms from four independent loci significantly associated with brain aging, three of which were novel. By comparing association results based on the CNN-derived PBA to those based on a linear regression-derived PBA, we concluded that a more accurate PBA enables the discovery of novel genetic associations. Our results may be valuable for identifying other lifestyle factors associated with brain aging.

Neuromarkers from Whole-Brain Functional Connectivity Reveal the Cognitive Recovery Scheme for Overt Hepatic Encephalopathy after Liver Transplantation.

Neurocognitive impairment is present in cirrhosis and may be more severe in cirrhosis with overt hepatic encephalopathy (OHE). Liver transplantation (LT) can restore liver function, but how it reverses the impaired brain function is still unclear. MRI of resting-state functional connectivity can help reveal the underlying mechanisms that lead to these cognitive deficits and cognitive recovery. In this study, 64 patients with cirrhosis (28 with OHE; 36 without OHE) and 32 healthy control subjects were recruited for resting-state fMRI. The patients were scanned before and after LT. We evaluated presurgical and postsurgical neurocognitive performance in cirrhosis patients using psychomotor tests. Network-based statistics found significant disrupted connectivity in both groups of cirrhotic patients, with OHE and without OHE, compared with control subjects. However, the presurgical connectivity disruption in patients with OHE affected a greater number of connections than those without OHE. The decrease in functional connectivity for both OHE and non-OHE patient groups was reversed after LT to the level of control subjects. An additional hyperconnected network (i.e., higher connected than control subjects) was observed in OHE patients after LT. Regarding the neural-behavior relationship, the functional network that predicted cognitive performance in healthy individuals showed no correlation in presurgical cirrhotic patients. The impaired neural-behavior relationship was re-established after LT for non-OHE patients, but not for OHE patients. OHE patients displayed abnormal hyperconnectivity and a persistently impaired neural-behavior relationship after LT. Our results suggest that patients with OHE may undergo a different trajectory of postsurgical neurofunctional recovery compared with those without, which needs further clarification in future studies.

Predicting Successful Generation and Inhibition of Seizure-like Afterdischarges and Mapping Their Seizure Networks Using fMRI.

To understand the conditions necessary to initiate and terminate seizures, we investigate optogenetically induced hippocampal seizures with LFP, fMRI, and optogenetic inhibition. During afterdischarge induction using optogenetics, LFP recordings show that stimulations with earlier ictal onset times are more likely to result in afterdischarges and are more difficult to curtail with optogenetic inhibition. These results are generalizable across two initiation sites, the dorsal and ventral hippocampus. fMRI shows that afterdischarges initiated from the dorsal or ventral hippocampus exhibit distinct networks. Short-duration seizures initiated in the dorsal and ventral hippocampus are unilateral and bilateral, respectively, while longer-duration afterdischarges recruit broader, bilateral networks. When optogenetic inhibition is ineffective at stopping seizures, the network activity spreads more extensively but largely overlaps with the network activity associated with seizures that could be curtailed. These results provide insights into how seizures can be inhibited, which has implications for targeted seizure interventions.

The LONI QC System: A Semi-Automated, Web-Based and Freely-Available Environment for the Comprehensive Quality Control of Neuroimaging Data.

Quantifying, controlling, and monitoring image quality is an essential prerequisite for ensuring the validity and reproducibility of many types of neuroimaging data analyses. Implementation of quality control (QC) procedures is the key to ensuring that neuroimaging data are of high-quality and their validity in the subsequent analyses. We introduce the QC system of the Laboratory of Neuro Imaging (LONI): a web-based system featuring a workflow for the assessment of various modality and contrast brain imaging data. The design allows users to anonymously upload imaging data to the LONI-QC system. It then computes an exhaustive set of QC metrics which aids users to perform a standardized QC by generating a range of scalar and vector statistics. These procedures are performed in parallel using a large compute cluster. Finally, the system offers an automated QC procedure for structural MRI, which can flag each QC metric as being 'good' or 'bad.' Validation using various sets of data acquired from a single scanner and from multiple sites demonstrated the reproducibility of our QC metrics, and the sensitivity and specificity of the proposed Auto QC to 'bad' quality images in comparison to visual inspection. To the best of our knowledge, LONI-QC is the first online QC system that uniquely supports the variety of functionality where we compute numerous QC metrics and perform visual/automated image QC of multi-contrast and multi-modal brain imaging data. The LONI-QC system has been used to assess the quality of large neuroimaging datasets acquired as part of various multi-site studies such as the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Study and the Alzheimer's Disease Neuroimaging Initiative (ADNI). LONI-QC's functionality is freely available to users worldwide and its adoption by imaging researchers is likely to contribute substantially to upholding high standards of brain image data quality and to implementing these standards across the neuroimaging community.

Cochlear Implantation in Postlingually Deaf Adults is Time-sensitive Towards Positive Outcome: Prediction using Advanced Machine Learning Techniques.

Given our aging society and the prevalence of age-related hearing loss that often develops during adulthood, hearing loss is a common public health issue affecting almost all older adults. Moderate-to-moderately severe hearing loss can usually be corrected with hearing aids; however, severe-to-profound hearing loss often requires a cochlear implant (CI). However, post-operative CI results vary, and the performance of the previous prediction models is limited, indicating that a new approach is needed. For postlingually deaf adults (n de120) who received CI with full insertion, we predicted CI outcomes using a Random-Forest Regression (RFR) model and investigated the effect of preoperative factors on CI outcomes. Postoperative word recognition scores (WRS) served as the dependent variable to predict. Predictors included duration of deafness (DoD), age at CI operation (ageCI), duration of hearing-aid use (DoHA), preoperative hearing threshold and sentence recognition score. Prediction accuracy was evaluated using mean absolute error (MAE) and Pearson's correlation coefficient r between the true WRS and predicted WRS. The fitting using a linear model resulted in prediction of WRS with r = 0.7 and MAE = 15.6 ± 9. RFR outperformed the linear model (r = 0.96, MAE = 6.1 ± 4.7, p < 0.00001). Cross-hospital data validation showed reliable performance using RFR (r = 0.91, MAE = 9.6 ± 5.2). The contribution of DoD to prediction was the highest (MAE increase when omitted: 14.8), followed by ageCI (8.9) and DoHA (7.5). After CI, patients with DoD < 10 years presented better WRSs and smaller variations (p < 0.01) than those with longer DoD. Better WRS was also explained by younger age at CI and longer-term DoHA. Machine learning demonstrated a robust prediction performance for CI outcomes in postlingually deaf adults across different institutes, providing a reference value for counseling patients considering CI. Health care providers should be aware that the patients with severe-to-profound hearing loss who cannot have benefit from hearing aids need to proceed with CI as soon as possible and should continue using hearing aids until after CI operation.

Activation of Direct and Indirect Pathway Medium Spiny Neurons Drives Distinct Brain-wide Responses.

A central theory of basal ganglia function is that striatal neurons expressing the D1 and D2 dopamine receptors exert opposing brain-wide influences. However, the causal influence of each population has never been measured at the whole-brain scale. Here, we selectively stimulated D1 or D2 receptor-expressing neurons while visualizing whole-brain activity with fMRI. Excitation of either inhibitory population evoked robust positive BOLD signals within striatum, while downstream regions exhibited significantly different and generally opposing responses consistent with-though not easily predicted from-contemporary models of basal ganglia function. Importantly, positive and negative signals within the striatum, thalamus, GPi, and STN were all associated with increases and decreases in single-unit activity, respectively. These findings provide direct evidence for the opposing influence of D1 and D2 receptor-expressing striatal neurons on brain-wide circuitry and extend the interpretability of fMRI studies by defining cell-type-specific contributions to the BOLD signal.

In vivo imaging of transplanted stem cells in the central nervous system.

In vivo imaging is increasingly being utilized in studies investigating stem cell-based treatments for neurological disorders. Direct labeling is used in preclinical and clinical studies to track the fate of transplanted cells. To further determine cell viability, experimental studies are able to take advantage of reporter gene technologies. Structural and functional brain imaging can also be used alongside cell imaging as biomarkers of treatment efficacy. Furthermore, it is possible that new imaging techniques could be used to monitor functional integration of stem cell-derived cells with the host nervous system. In this review, we examine recent developments in these areas and identify promising directions for future research at the interface of stem cell therapies and neuroimaging.

Enrichment and training improve cognition in rats with cortical malformations.

Children with malformations of cortical development (MCD) frequently have associated cognitive impairments which reduce quality of life. We hypothesized that cognitive deficits associated with MCD can be improved with environmental manipulation or additional training. The E17 methylazoxymethanol acetate (MAM) exposure model bears many anatomical hallmarks seen in human MCDs as well as similar behavioral and cognitive deficits. We divided control and MAM exposed Sprague-Dawley rats into enriched and non-enriched groups and tested performance in the Morris water maze. Another group similarly divided underwent sociability testing and also underwent Magnetic Resonance Imaging (MRI) scans pre and post enrichment. A third group of control and MAM rats without enrichment were trained until they reached criterion on the place avoidance task. MAM rats had impaired performance on spatial tasks and enrichment improved performance of both control and MAM animals. Although MAM rats did not have a deficit in sociability they showed similar improvement with enrichment as controls. MRI revealed a whole brain volume decrease with MAM exposure, and an increase in both MAM and control enriched volumes in comparison to non-enriched animals. In the place avoidance task, MAM rats required approximately 3 times as long to reach criterion as control animals, but with additional training were able to reach control performance. Environmental manipulation and additional training can improve cognition in a rodent MCD model. We therefore suggest that patients with MCD may benefit from appropriate alterations in educational strategies, social interaction and environment. These factors should be considered in therapeutic strategies.