A rapid, hippocampus-dependent, item-memory signal that initiates context memory in humans.

The hippocampus, a structure located in the temporal lobes of the brain, is critical for the ability to recollect contextual details of past episodes. It is still debated whether the hippocampus also enables recognition memory for previously encountered context-free items. Brain imaging and neuropsychological patient studies have both individually provided conflicting answers to this question. We overcame the individual limitations of imaging and behavioral patient studies by combining them and observed a novel relationship between item memory and the hippocampus. We show that interindividual variability of hippocampal volumes in a large patient population with graded levels of hippocampal volume loss and controls correlates with context, but not item-memory performance. Nevertheless, concurrent measures of brain activity using magnetoencephalography reveal an early (350 ms) but sustained hippocampus-dependent signal that evolves from an item signal into a context memory signal. This is temporally distinct from an item-memory signal that is not hippocampus dependent. Thus, we provide evidence for a hippocampus-dependent item-memory process that initiates context retrieval without making a substantial contribution to item recognition performance. Our results reconcile contradictory evidence concerning hippocampal involvement in item memory and show that hippocampus-dependent mnemonic processes are more rapid than previously believed

Detection of Cerebral Microbleeds With Venous Connection at 7-Tesla MRI

Objective: Cerebral microbleeds (MBs) are a common finding in patients with cerebral small vessel disease (CSVD) and Alzheimer disease as well as in healthy elderly people, but their pathophysiology remains unclear. To investigate a possible role of veins in the development of MBs, we performed an exploratory study, assessing in vivo presence of MBs with a direct connection to a vein. Methods: 7-Tesla (7T) MRI was conducted and MBs were counted on quantitative susceptibility mapping (QSM). A submillimeter resolution QSM-based venogram allowed identification of MBs with a direct spatial connection to a vein. Results: A total of 51 people (mean age [SD] 70.5 [8.6] years, 37% female) participated in the study: 20 had CSVD (cerebral amyloid angiopathy [CAA] with strictly lobar MBs [n = 8], hypertensive arteriopathy [HA] with strictly deep MBs [n = 5], or mixed lobar and deep MBs [n = 7], 72.4 [6.1] years, 30% female) and 31 were healthy controls (69.4 [9.9] years, 42% female). In our cohort, we counted a total of 96 MBs with a venous connection, representing 14% of all detected MBs on 7T QSM. Most venous MBs (86%, n = 83) were observed in lobar locations and all of these were cortical. Patients with CAA showed the highest ratio of venous to total MBs (19%) (HA = 9%, mixed = 18%, controls = 5%). Conclusion: Our findings establish a link between cerebral MBs and the venous vasculature, pointing towards a possible contribution of veins to CSVD in general and to CAA in particular. Pathologic studies are needed to confirm our observations

Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution;(B) incorporating demographics & clinical covariates;(C) the impact of the size of the training data set;(D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of sample predictions. The highest performance for A 42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status

Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution;(B) incorporating demographics & clinical covariates;(C) the impact of the size of the training data set;(D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of sample predictions. The highest performance for A 42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status

Widespread sensorimotor and frontal cortical atrophy in Amyotrophic Lateral Sclerosis

BACKGROUND: Widespread cortical atrophy in Amyotrophic Lateral Sclerosis (ALS) has been described in neuropathological studies. The presence of cortical atrophy in conventional and scientific neuroimaging has been a matter of debate. In studies using computertomography, positron emission tomography, proton magnetic resonance spectroscopy and conventional T2-weighted and proton-weighted images, results have been variable. Recent morphometric studies by magnetic resonance imaging have produced conflicting results regarding the extent of grey and white matter involvement in ALS patients. METHODS: The authors used optimized voxel-based morphometry as an unbiased whole brain approach to detect differences between regional grey and white matter volumes. Seventeen patients with a diagnosis of ALS according to El-Escorial criteria and seventeen age-matched controls received a high resolution anatomical T1 scan. RESULTS: In ALS patients regional grey matter volume (GMV) reductions were found in the pre- and postcentral gyrus bilaterally which extended to premotor, parietal and frontal regions bilaterally compared with controls (p < 0.05, corrected for the entire volume). The revised ALS functional rating scale showed a positive correlation with GMV reduction of the right medial frontal gyrus corresponding to the dorsolateral prefrontal cortex. No significant differences were found for white matter volumes or when grey and white matter density images were investigated. There were no further correlations with clinical variables found. CONCLUSION: In ALS patients, primary sensorimotor cortex atrophy can be regarded as a prominent feature of the disease. Supporting the concept of ALS being a multisytem disorder, our study provides further evidence for extramotor involvement which is widespread. The lack of correlation with common clinical variables probably reflects the fact that heterogeneous disease processes underlie ALS. The discrepancy within all published morphometric studies in ALS so far may be related to differences in patient cohorts and several methodological factors of the data analysis process. Longitudinal studies are required to further clarify the time course and distribution of grey and white matter pathology during the course of ALS

Ventromedial Prefrontal Cortex Activation Is Associated with Memory Formation for Predictable Rewards

During reinforcement learning, dopamine release shifts from the moment of reward consumption to the time point when the reward can be predicted. Previous studies provide consistent evidence that reward-predicting cues enhance long-term memory (LTM) formation of these items via dopaminergic projections to the ventral striatum. However, it is less clear whether memory for items that do not precede a reward but are directly associated with reward consumption is also facilitated. Here, we investigated this question in an fMRI paradigm in which LTM for reward-predicting and neutral cues was compared to LTM for items presented during consumption of reliably predictable as compared to less predictable rewards. We observed activation of the ventral striatum and enhanced memory formation during reward anticipation. During processing of less predictable as compared to reliably predictable rewards, the ventral striatum was activated as well, but items associated with less predictable outcomes were remembered worse than items associated with reliably predictable outcomes. Processing of reliably predictable rewards activated the ventromedial prefrontal cortex (vmPFC), and vmPFC BOLD responses were associated with successful memory formation of these items. Taken together, these findings show that consumption of reliably predictable rewards facilitates LTM formation and is associated with activation of the vmPFC

Genetic Variation of the Serotonin 2a Receptor Affects Hippocampal Novelty Processing in Humans

Serotonin (5-hydroxytryptamine, 5-HT) is an important neuromodulator in learning and memory processes. A functional genetic polymorphism of the 5-HT 2a receptor (5-HTR2a His452Tyr), which leads to blunted intracellular signaling, has previously been associated with explicit memory performance in several independent cohorts, but the underlying neural mechanisms are thus far unclear. The human hippocampus plays a critical role in memory, particularly in the detection and encoding of novel information. Here we investigated the relationship of 5-HTR2a His452Tyr and hippocampal novelty processing in 41 young, healthy subjects using functional magnetic resonance imaging (fMRI). Participants performed a novelty/familiarity task with complex scene stimuli, which was followed by a delayed recognition memory test 24 hours later. Compared to His homozygotes, Tyr carriers exhibited a diminished hippocampal response to novel stimuli and a higher tendency to judge novel stimuli as familiar during delayed recognition. Across the cohort, the false alarm rate during delayed recognition correlated negatively with the hippocampal novelty response. Our results suggest that previously reported effects of 5-HTR2a on explicit memory performance may, at least in part, be mediated by alterations of hippocampal novelty processing

Classification and Lateralization of Temporal Lobe Epilepsies with and without Hippocampal Atrophy Based on Whole-Brain Automatic MRI Segmentation

Brain images contain information suitable for automatically sorting subjects into categories such as healthy controls and patients. We sought to identify morphometric criteria for distinguishing controls (n = 28) from patients with unilateral temporal lobe epilepsy (TLE), 60 with and 20 without hippocampal atrophy (TLE-HA and TLE-N, respectively), and for determining the presumed side of seizure onset. The framework employs multi-atlas segmentation to estimate the volumes of 83 brain structures. A kernel-based separability criterion was then used to identify structures whose volumes discriminate between the groups. Next, we applied support vector machines (SVM) to the selected set for classification on the basis of volumes. We also computed pairwise similarities between all subjects and used spectral analysis to convert these into per-subject features. SVM was again applied to these feature data. After training on a subgroup, all TLE-HA patients were correctly distinguished from controls, achieving an accuracy of 96 ± 2% in both classification schemes. For TLE-N patients, the accuracy was 86 ± 2% based on structural volumes and 91 ± 3% using spectral analysis. Structures discriminating between patients and controls were mainly localized ipsilaterally to the presumed seizure focus. For the TLE-HA group, they were mainly in the temporal lobe; for the TLE-N group they included orbitofrontal regions, as well as the ipsilateral substantia nigra. Correct lateralization of the presumed seizure onset zone was achieved using hippocampi and parahippocampal gyri in all TLE-HA patients using either classification scheme; in the TLE-N patients, lateralization was accurate based on structural volumes in 86 ± 4%, and in 94 ± 4% with the spectral analysis approach. Unilateral TLE has imaging features that can be identified automatically, even when they are invisible to human experts. Such morphometric image features may serve as classification and lateralization criteria. The technique also detects unsuspected distinguishing features like the substantia nigra, warranting further study

Early Alterations in Hippocampal Circuitry and Theta Rhythm Generation in a Mouse Model of Prenatal Infection: Implications for Schizophrenia

Post-mortem studies suggest that GABAergic neurotransmission is impaired in schizophrenia. However, it remains unclear if these changes occur early during development and how they impact overall network activity. To investigate this, we used a mouse model of prenatal infection with the viral mimic, polyriboinosinic–polyribocytidilic acid (poly I∶C), a model based on epidemiological evidence that an immune challenge during pregnancy increases the prevalence of schizophrenia in the offspring. We found that prenatal infection reduced the density of parvalbumin- but not somatostatin-positive interneurons in the CA1 area of the hippocampus and strongly reduced the strength of inhibition early during postnatal development. Furthermore, using an intact hippocampal preparation in vitro, we found reduced theta oscillation generated in the CA1 area. Taken together, these results suggest that redistribution in excitatory and inhibitory transmission locally in the CA1 is associated with a significant alteration in network function. Furthermore, given the role of theta rhythm in memory, our results demonstrate how a risk factor for schizophrenia can affect network function early in development that could contribute to cognitive deficits observed later in the disease