Lower entorhinal cortex thickness is associated with greater financial exploitation vulnerability in cognitively unimpaired older adults.

Research suggests that increased financial exploitation vulnerability due to declining decision making may be an early behavioral manifestation of brain changes occurring in preclinical Alzheimer's disease. One of the earliest documented brain changes during the preclinical phase is neurodegeneration in the entorhinal cortex. The objective of the current study was to examine the association between a measure of financial exploitation vulnerability and thickness in the entorhinal cortex in 97 cognitively unimpaired older adults. We also investigated financial exploitation vulnerability associations with frontal regions typically associated with decision making (e.g. dorsolateral and ventromedial prefrontal cortices), and additionally examined the interactive effect of age and cortical thickness on financial exploitation vulnerability. Results showed that greater financial exploitation vulnerability was associated with significantly lower entorhinal cortex thickness. There was a significant interaction between age and entorhinal cortex thickness on financial exploitation vulnerability, whereby lower entorhinal cortex thickness was associated with greater financial exploitation vulnerability in older participants. When the group was divided by age using a median split (70+ and <70 years old), lower entorhinal cortex thickness was associated with greater vulnerability only in the older group. Collectively, these findings suggest that financial exploitation vulnerability may serve as a behavioral manifestation of entorhinal cortex thinning, a phenomenon observed in suboptimal brain aging and preclinical Alzheimer's disease.

Differential Laminar Activation Dissociates Encoding and Retrieval in the Human Medial and Lateral Entorhinal Cortex.

The hierarchically organized structures of the medial temporal lobe are critically important for episodic memory function. Accumulating evidence suggests dissociable information processing pathways are maintained throughout these structures including in the medial and lateral entorhinal cortex. Cortical layers provide an additional dimension of dissociation as the primary input to the hippocampus derives from layer 2 neurons in the entorhinal cortex, whereas the deeper layers primarily receive output from the hippocampus. Here, novel high-resolution T2-prepared functional MRI methods were successfully used to mitigate susceptibility artifacts typically affecting MRI signals in this region providing uniform sensitivity across the medial and lateral entorhinal cortex. During the performance of a memory task, healthy human subjects (age 25-33 years, mean age 28.2 ± 3.3 years, 4 female) showed differential functional activation in the superficial and deep layers of the entorhinal cortex associated with task-related encoding and retrieval conditions, respectively. The methods provided here offer an approach to probe layer-specific activation in normal cognition and conditions contributing to memory impairment.SIGNIFICANCE STATEMENT This study provides new evidence for differential neuronal activation in the superficial versus deep layers of the entorhinal cortex associated with encoding and retrieval memory processes, respectively, in cognitively normal adults. The study further shows that this dissociation can be observed in both the medial and the lateral entorhinal cortex. The study was achieved by using a novel functional MRI method allowing us to measure robust functional MRI signals in both the medial and lateral entorhinal cortex that was not possible in previous studies. The methodology established here in healthy human subjects lays a solid foundation for subsequent studies investigating layer-specific and region-specific changes in the entorhinal cortex associated with memory impairment in various conditions such as Alzheimer's disease.

A combined DTI-fMRI approach for optimizing the delineation of posteromedial versus anterolateral entorhinal cortex.

In the entorhinal cortex (EC), attempts have been made to identify the human homologue regions of the medial (MEC) and lateral (LEC) subregions using either functional magnetic resonance imaging (fMRI) or diffusion tensor imaging (DTI). However, there are still discrepancies between entorhinal subdivisions depending on the choice of connectivity seed regions and the imaging modality used. While DTI can be used to follow the white matter tracts of the brain, fMRI can identify functionally connected brain regions. In this study, we used both DTI and resting-state fMRI in 103 healthy adults to investigate both structural and functional connectivity between the EC and associated cortical brain regions. Differential connectivity with these regions was then used to predict the locations of the human homologues of MEC and LEC. Our results from combining DTI and fMRI support a subdivision into posteromedial (pmEC) and anterolateral (alEC) EC and reveal a confined border between the pmEC and alEC. Furthermore, the EC subregions obtained by either imaging modality showed similar distinct whole-brain connectivity profiles. Optimizing the delineation of the human homologues of MEC and LEC with a combined, cross-validated DTI-fMRI approach allows to define a likely border between the two subdivisions and has implications for both cognitive and translational neuroscience research.

Neuropsychiatric symptoms: Risk factor or disease marker? A study of structural imaging biomarkers of Alzheimer's disease and incident cognitive decline.

Neuropsychiatric symptoms (NPS) are risk factors for Alzheimer's disease (AD) but can also manifest secondary to AD pathology. Mild behavioral impairment (MBI) refers to later-life emergent and persistent NPS that may mark early-stage AD. To distinguish MBI from NPS that are transient or which represent psychiatric conditions (non-MBI NPS), we investigated the effect of applying MBI criteria on NPS associations with AD structural imaging biomarkers and incident cognitive decline. Data for participants (n = 1273) with normal cognition (NC) or mild cognitive impairment (MCI) in the National Alzheimer's Coordinating Center Uniform Data Set were analyzed. NPS status (MBI, non-MBI NPS) was derived from the Neuropsychiatric Inventory Questionnaire and psychiatric history. Normalized measures of bilateral hippocampal (HPC) and entorhinal cortex (EC) volume, and AD meta-region of interest (ROI) mean cortical thickness were acquired from T1-weighted magnetic resonance imaging scans. Multivariable linear and Cox regressions examined NPS associations with imaging biomarkers and incident cognitive decline, respectively. MBI was associated with lower volume and cortical thickness in all ROIs in both NC and MCI, except for EC volume in NC. Non-MBI NPS were only associated with lower HPC volume in NC. Although both of the NPS groups showed higher hazards for MCI/dementia than No NPS, MBI participants showed more rapid decline. Although both types of NPS were linked to HPC atrophy, only NPS that emerged and persisted in later-life, consistent with MBI criteria, were related to AD neurodegenerative patterns beyond the HPC. Moreover, MBI predicted faster progression to dementia than non-MBI NPS.

The Effect of Segmentation Method on Medial Temporal Lobe Subregion Volumes in Aging.

Early stages of Alzheimer's disease (AD) are associated with volume reductions in specific subregions of the medial temporal lobe (MTL). Using a manual segmentation method-the Olsen-Amaral-Palombo (OAP) protocol-previous work in healthy older adults showed that reductions in grey matter volumes in MTL subregions were associated with lower scores on the Montreal Cognitive Assessment (MoCA), suggesting atrophy may occur prior to diagnosis of mild cognitive impairment, a condition that often progresses to AD. However, current "gold standard" manual segmentation methods are labour intensive and time consuming. Here, we examined the utility of Automatic Segmentation of Hippocampal Subfields (ASHS) to detect volumetric differences in MTL subregions of healthy older adults who varied in cognitive status as determined by the MoCA. We trained ASHS on the OAP protocol to create the ASHS-OAP atlas and then examined how well automated segmentation replicated manual segmentation. Volumetric measures obtained from the ASHS-OAP atlas were also contrasted against those from the ASHS-PMC atlas, a widely used atlas provided by the ASHS team. The pattern of volumetric results was similar between the ASHS-OAP atlas and manual segmentation for anterolateral entorhinal cortex and perirhinal cortex, suggesting that ASHS-OAP is a viable alternative to current manual segmentation methods for detecting group differences based on cognitive status. Although ASHS-OAP and ASHS-PMC produced varying volumes for most regions of interest, they both identified early signs of neurodegeneration in CA2/CA3/DG and identified marginal differences in entorhinal cortex. Our findings highlight the utility of automated segmentation methods but still underscore the need for a unified and harmonized MTL segmentation atlas.

Sex significantly predicts medial temporal volume when controlling for the influence of ApoE4 biomarker and demographic variables: A cross-ethnic comparison.

OBJECTIVE: To explore the relationship between age, education, sex, and ApoE4 (+) status to brain volume among a cohort with amnestic mild cognitive impairment (aMCI). METHOD: One hundred and twenty-three participants were stratified into Hispanic (n = 75) and White non-Hispanic (WNH, N = 48). Multiple linear regression analyses were conducted with age, education, sex, and ApoE4 status as predictor variables and left and right combined MRI volumes of the hippocampus, parahippocampus, and entorhinal cortex as dependent variables. Variations in head sizes were corrected by normalization with a total intracranial volume measurement. RESULTS: Bonferroni-corrected results indicated that when controlling for ApoE4 status, education, and age, sex was a significant predictor of hippocampal volume among the Hispanic group (ß = .000464, R2 = .196, p < .01) and the WNH group (ß = .000455, R2 = .195, p < .05). Education (ß = .000028, R2 = .168, p < .01) and sex (ß = .000261, R2 = .168, p < .01) were significant predictors of parahippocampal volume among the Hispanic MCI group when controlling for the effects of ApoE4 status and age. One-way ANCOVAs comparing hippocampal and parahippocampal volume between males and females within groups revealed that females had significantly larger hippocampal volumes (p < .05). Hispanic females had significantly larger hippocampal (p < .001) and parahippocampal (p < .05) volume compared to males. No sex differences in parahippocampal volume were noted among WNHs. CONCLUSIONS: Biological sex, rather than ApoE4 status, was a greater predictor of hippocampal volume among Hispanic and WNH females. These findings add to the mixed literature on sex differences in dementia research and highlight continued emphasis on ethnic populations to elucidate on neurodegenerative disparities.

Single voxel autocorrelation uncovers gradients of temporal dynamics in the hippocampus and entorhinal cortex during rest and navigation.

During navigation, information at multiple scales needs to be integrated. Single-unit recordings in rodents suggest that gradients of temporal dynamics in the hippocampus and entorhinal cortex support this integration. In humans, gradients of representation are observed, such that granularity of information represented increases along the long axis of the hippocampus. The neural underpinnings of this gradient in humans, however, are still unknown. Current research is limited by coarse fMRI analysis techniques that obscure the activity of individual voxels, preventing investigation of how moment-to-moment changes in brain signal are organized and how they are related to behavior. Here, we measured the signal stability of single voxels over time to uncover previously unappreciated gradients of temporal dynamics in the hippocampus and entorhinal cortex. Using our novel, single voxel autocorrelation technique, we show a medial-lateral hippocampal gradient, as well as a continuous autocorrelation gradient along the anterolateral-posteromedial entorhinal extent. Importantly, we show that autocorrelation in the anterior-medial hippocampus was modulated by navigational difficulty, providing the first evidence that changes in signal stability in single voxels are relevant for behavior. This work opens the door for future research on how temporal gradients within these structures support the integration of information for goal-directed behavior.

Associations between white matter hyperintensities, lacunes, entorhinal cortex thickness, declarative memory and leisure activity in cognitively healthy older adults: A 7-year study.

INTRODUCTION: Cerebral small vessel disease (cSVD) is a growing epidemic that affects brain health and cognition. Therefore, a more profound understanding of the interplay between cSVD, brain atrophy, and cognition in healthy aging is of great importance. In this study, we examined the association between white matter hyperintensities (WMH) volume, number of lacunes, entorhinal cortex (EC) thickness, and declarative memory in cognitively healthy older adults over a seven-year period, controlling for possible confounding factors. Because there is no cure for cSVD to date, the neuroprotective potential of an active lifestyle has been suggested. Supporting evidence, however, is scarce. Therefore, a second objective of this study is to examine the relationship between leisure activities, cSVD, EC thickness, and declarative memory. METHODS: We used a longitudinal dataset, which consisted of five measurement time points of structural MRI and psychometric cognitive ability and survey data, collected from a sample of healthy older adults (baseline N = 231, age range: 64-87 years, age M = 70.8 years), to investigate associations between cSVD MRI markers, EC thickness and verbal and figural memory performance. Further, we computed physical, social, and cognitive leisure activity scores from survey-based assessments and examined their associations with brain structure and declarative memory. To provide more accurate estimates of the trajectories and cross-domain correlations, we applied latent growth curve models controlling for potential confounders. RESULTS: Less age-related thinning of the right (ß = 0.92, p<.05) and left EC (ß = 0.82, p<.05) was related to less declarative memory decline; and a thicker EC at baseline predicted less declarative memory loss (ß = 0.54, p<.05). Higher baseline levels of physical (ß = 0.24, p<.05), and social leisure activity (ß = 0.27, p<.01) predicted less thinning of right EC. No relation was found between WMH or lacunes and declarative memory or between leisure activity and declarative memory. Higher education was initially related to more physical activity (ß = 0.16, p<.05) and better declarative memory (ß = 0.23, p<.001), which, however, declined steeper in participants with higher education (ß = -.35, p<.05). Obese participants were less physically (ß = -.18, p<.01) and socially active (ß = -.13, p<.05) and had thinner left EC (ß = -.14, p<.05) at baseline. Antihypertensive medication use (ß = -.26, p<.05), and light-to-moderate alcohol consumption (ß = -.40, p<.001) were associated with a smaller increase in the number of lacunes whereas a larger increase in the number of lacunes was observed in current smokers (ß = 0.30, p<.05). CONCLUSIONS: Our results suggest complex relationships between cSVD MRI markers (total WMH, number of lacunes, right and left EC thickness), declarative memory, and confounding factors such as antihypertensive medication, obesity, and leisure activitiy. Thus, leisure activities and having good cognitive reserve counteracting this neurodegeneration. Several confounding factors seem to contribute to the extent or progression/decline of cSVD, which needs further investigation in the future. Since there is still no cure for cSVD, modifiable confounding factors should be studied more intensively in the future to maintain or promote brain health and thus cognitive abilities in older adults.

An Optimized Deep Learning Model for Predicting Mild Cognitive Impairment Using Structural MRI.

Early diagnosis of mild cognitive impairment (MCI) with magnetic resonance imaging (MRI) has been shown to positively affect patients' lives. To save time and costs associated with clinical investigation, deep learning approaches have been used widely to predict MCI. This study proposes optimized deep learning models for differentiating between MCI and normal control samples. In previous studies, the hippocampus region located in the brain is used extensively to diagnose MCI. The entorhinal cortex is a promising area for diagnosing MCI since severe atrophy is observed when diagnosing the disease before the shrinkage of the hippocampus. Due to the small size of the entorhinal cortex area relative to the hippocampus, limited research has been conducted on the entorhinal cortex brain region for predicting MCI. This study involves the construction of a dataset containing only the entorhinal cortex area to implement the classification system. To extract the features of the entorhinal cortex area, three different neural network architectures are optimized independently: VGG16, Inception-V3, and ResNet50. The best outcomes were achieved utilizing the convolution neural network classifier and the Inception-V3 architecture for feature extraction, with accuracy, sensitivity, specificity, and area under the curve scores of 70%, 90%, 54%, and 69%, respectively. Furthermore, the model has an acceptable balance between precision and recall, achieving an F1 score of 73%. The results of this study validate the effectiveness of our approach in predicting MCI and may contribute to diagnosing MCI through MRI.

Reduced Repetition Suppression in Aging is Driven by Tau-Related Hyperactivity in Medial Temporal Lobe.

Tau deposition begins in the medial temporal lobe (MTL) in aging and Alzheimer's disease (AD), and MTL neural dysfunction is commonly observed in these groups. However, the association between tau and MTL neural activity has not been fully characterized. We investigated the effects of tau on repetition suppression, the reduction of activity for repeated stimulus presentations compared to novel stimuli. We used task-based functional MRI (fMRI) to assess MTL subregional activity in 21 young adults (YA) and 45 cognitively normal human older adults (OA; total sample: 37 females, 29 males). AD pathology was measured with position emission tomography (PET), using 18F-Flortaucipir for tau and 11C-Pittsburgh compound B (PiB) for amyloid-ß (Aß). The MTL was segmented into six subregions using high-resolution structural images. We compared the effects of low tau pathology, restricted to entorhinal cortex and hippocampus (Tau- OA), to high tau pathology, also occurring in temporal and limbic regions (Tau+ OA). Low levels of tau (Tau- OA vs YA) were associated with reduced repetition suppression activity specifically in anterolateral entorhinal cortex (alEC) and hippocampus, the first regions to accumulate tau. High tau pathology (Tau+ vs Tau- OA) was associated with widespread reductions in repetition suppression across MTL. Further analyses indicated that reduced repetition suppression was driven by hyperactivity to repeated stimuli, rather than decreased activity to novel stimuli. Increased activation was associated with entorhinal tau, but not Aß. These findings reveal a link between tau deposition and neural dysfunction in MTL, in which tau-related hyperactivity prevents deactivation to repeated stimuli, leading to reduced repetition suppression.SIGNIFICANCE STATEMENT Abnormal neural activity occurs in the medial temporal lobe (MTL) in aging and Alzheimer's disease (AD). Because tau pathology first deposits in the MTL in aging, this altered activity may be due to local tau pathology, and distinct MTL subregions may be differentially vulnerable. We demonstrate that in older adults (OAs) with low tau pathology, there are focal alterations in activity in MTL subregions that first develop tau pathology, while OAs with high tau pathology have aberrant activity throughout MTL. Tau was associated with hyperactivity to repeated stimulus presentations, leading to reduced repetition suppression, the discrimination between novel and repeated stimuli. Our data suggest that tau deposition is related to abnormal activity in MTL before the onset of cognitive decline.

Regional Tau Effects on Prospective Cognitive Change in Cognitively Normal Older Adults.

Studies suggest that tau deposition starts in the anterolateral entorhinal cortex (EC) with normal aging, and that the presence of ß-amyloid (Aß) facilitates its spread to neocortex, which may reflect the beginning of Alzheimer's disease (AD). Functional connectivity between the anterolateral EC and the anterior-temporal (AT) memory network appears to drive higher tau deposition in AT than in the posterior-medial (PM) memory network. Here, we investigated whether this differential vulnerability to tau deposition may predict different cognitive consequences of EC, AT, and PM tau. Using 18F-flortaucipir (FTP) and 11C-Pittsburgh compound-B (PiB) positron emission tomography (PET) imaging, we measured tau and Aß in 124 cognitively normal human older adults (74 females, 50 males) followed for an average of 2.8 years for prospective cognition. We found that higher FTP in all three regions was individually related to faster memory decline, and that the effects of AT and PM FTP, but not EC, were driven by Aß+ individuals. Moreover, when we included all three FTP measures competitively in the same model, only AT FTP significantly predicted memory decline. Our data support a model whereby tau, facilitated by Aß, transits from EC to cortical regions that are most closely associated with the anterolateral EC, which specifically affects memory in the initial stage of AD. Memory also appears to be affected by EC tau in the absence of Aß, which may be less clinically consequential. These findings may provide clarification of differences between normal aging and AD, and elucidate the transition between the two stages.SIGNIFICANCE STATEMENT Tau and ß-amyloid (Aß) are hallmarks of Alzheimer's disease (AD) but are also found in cognitively normal people. It is unclear whether, and how, this early deposition of tau and Aß may affect cognition in normal aging and the asymptomatic stage of AD. We show that tau deposition in the entorhinal cortex (EC), which is common in advanced age, predicts memory decline in older adults independent of Aß, likely reflecting normal, age-related memory loss. In contrast, tau in anterior-temporal (AT) regions is most predictive of memory decline in Aß+ individuals. These data support the idea that tau preferentially spreads to specific cortical regions, likely through functional connections, which plays a primary role in memory decline in the early stage of AD.

Assessing mild cognitive impairment using object-location memory in immersive virtual environments.

Pathological changes in the medial temporal lobe (MTL) are found in the early stages of Alzheimer's disease (AD) and aging. The earliest pathological accumulation of tau colocalizes with the areas of the MTL involved in object processing as part of a wider anterolateral network. Here, we sought to assess the diagnostic potential of memory for object locations in iVR environments in individuals at high risk of AD dementia (amnestic mild cognitive impairment [aMCI] n = 23) as compared to age-related cognitive decline. Consistent with our primary hypothesis that early AD would be associated with impaired object location, aMCI patients exhibited impaired spatial feature binding. Compared to both older (n = 24) and younger (n = 53) controls, aMCI patients, recalled object locations with significantly less accuracy (p < .001), with a trend toward an impaired identification of the object's correct context (p = .05). Importantly, these findings were not explained by deficits in object recognition (p = .6). These deficits differentiated aMCI from controls with greater accuracy (AUC = 0.89) than the standard neuropsychological tests. Within the aMCI group, 16 had CSF biomarkers indicative of their likely AD status (MCI+ n = 9 vs. MCI- n = 7). MCI+ showed lower accuracy in the object-context association than MCI- (p = .03) suggesting a selective deficit in object-context binding postulated to be associated with anterior-temporal areas. MRI volumetric analysis across healthy older participants and aMCI revealed that test performance positively correlates with lateral entorhinal cortex volumes (p < .05) and hippocampus volumes (p < .01), consistent with their hypothesized role in binding contextual and spatial information with object identity. Our results indicate that tests relying on the anterolateral object processing stream, and in particular requiring successful binding of an object with spatial information, may aid detection of pre-dementia AD due to the underlying early spread of tau pathology.

Right entorhinal cortical thickness is associated with Mini-Mental State Examination scores from multi-country datasets using MRI.

PURPOSE: To discover common biomarkers correlating with the Mini-Mental State Examination (MMSE) scores from multi-country MRI datasets. METHODS: The first dataset comprised 112 subjects (49 men, 63 women; range, 46-94 years) at the National Hospital Organization Kyushu Medical Center. A second dataset comprised 300 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (177 men, 123 women; range, 57-91 years). Three-dimensional T1-weighted MR images were collected from both datasets. In total, 14 deep gray matter volumes and 70 cortical thicknesses were obtained from MR images using FreeSurfer software. Total hippocampal volume and the ratio of hippocampus to cerebral volume were also calculated. Correlations between each variable and MMSE scores were assessed using Pearson's correlation coefficient. Parameters with moderate correlation coefficients (r > 0.3) from each dataset were determined as independent variables and evaluated using general linear model (GLM) analyses. RESULTS: In Pearson's correlation coefficient, total and bilateral hippocampal volumes, right amygdala volume, and right entorhinal cortex (ERC) thickness showed moderate correlation coefficients (r > 0.3) with MMSE scores from the first dataset. The ADNI dataset showed moderate correlations with MMSE scores in more variables, including bilateral ERC thickness and hippocampal volume. GLM analysis revealed that right ERC thickness correlated significantly with MMSE score in both datasets. Cortical thicknesses of the left parahippocampal gyrus, left inferior parietal lobe, and right fusiform gyrus also significantly correlated with MMSE score in the ADNI dataset (p < 0.05). CONCLUSION: A positive correlation between right ERC thickness and MMSE score was identified from multi-country datasets.

Distance and Direction Codes Underlie Navigation of a Novel Semantic Space in the Human Brain.

A recent proposal posits that humans might use the same neuronal machinery to support the representation of both spatial and nonspatial information, organizing concepts and memories using spatial codes. This view predicts that the same neuronal coding schemes characterizing navigation in the physical space (tuned to distance and direction) should underlie navigation of abstract semantic spaces, even if they are categorical and labeled by symbols. We constructed an artificial semantic environment by parsing a bidimensional audiovisual object space into four labeled categories. Before and after a nonspatial symbolic categorization training, 25 adults (15 females) were presented with pseudorandom sequences of objects and words during a functional MRI session. We reasoned that subsequent presentations of stimuli (either objects or words) referring to different categories implied implicit movements in the novel semantic space, and that such movements subtended specific distances and directions. Using whole-brain fMRI adaptation and searchlight model-based representational similarity analysis, we found evidence of both distance-based and direction-based responses in brain regions typically involved in spatial navigation: the medial prefrontal cortex and the right entorhinal cortex (EHC). After training, both regions encoded the distances between concepts, making it possible to recover a faithful bidimensional representation of the semantic space directly from their multivariate activity patterns, whereas the right EHC also exhibited a periodic modulation as a function of traveled direction. Our results indicate that the brain regions and coding schemes supporting relations and movements between spatial locations in mammals are "recycled" in humans to represent a bidimensional multisensory conceptual space during a symbolic categorization task.SIGNIFICANCE STATEMENT The hippocampal formation and the medial prefrontal cortex of mammals represent the surrounding physical space by encoding distances and directions between locations. Recent works suggested that humans use the same neural machinery to organize their memories as points of an internal map of experiences. We asked whether the same brain regions and neural codes supporting spatial navigation are recruited when humans use language to organize their knowledge of the world in categorical semantic representations. Using fMRI, we show that the medial prefrontal cortex and the entorhinal portion of the hippocampal formation represent the distances and the movement directions between concepts of a novel audiovisual semantic space, and that it was possible to reconstruct, from neural data, their relationships in memory.

Structural connectivity-based segmentation of the human entorhinal cortex.

The medial (MEC) and lateral entorhinal cortex (LEC), widely studied in rodents, are well defined and characterized. In humans, however, the exact locations of their homologues remain uncertain. Previous functional magnetic resonance imaging (fMRI) studies have subdivided the human EC into posteromedial (pmEC) and anterolateral (alEC) parts, but uncertainty remains about the choice of imaging modality and seed regions, in particular in light of a substantial revision of the classical model of EC connectivity based on novel insights from rodent anatomy. Here, we used structural, not functional imaging, namely diffusion tensor imaging (DTI) and probabilistic tractography to segment the human EC based on differential connectivity to other brain regions known to project selectively to MEC or LEC. We defined MEC as more strongly connected with presubiculum and retrosplenial cortex (RSC), and LEC as more strongly connected with distal CA1 and proximal subiculum (dCA1pSub) and lateral orbitofrontal cortex (OFC). Although our DTI segmentation had a larger medial-lateral component than in the previous fMRI studies, our results show that the human MEC and LEC homologues have a border oriented both towards the posterior-anterior and medial-lateral axes, supporting the differentiation between pmEC and alEC.

Grid-like and distance codes for representing word meaning in the human brain.

Relational information about items in memory is thought to be represented in our brain thanks to an internal comprehensive model, also referred to as a "cognitive map". In the human neuroimaging literature, two signatures of bi-dimensional cognitive maps have been reported: the grid-like code and the distance-dependent code. While these kinds of representation were previously observed during spatial navigation and, more recently, during processing of perceptual stimuli, it is still an open question whether they also underlie the representation of the most basic items of language: words. Here we taught human participants the meaning of novel words as arbitrary labels for a set of audiovisual objects varying orthogonally in size and sound. The novel words were therefore conceivable as points in a navigable 2D map of meaning. While subjects performed a word comparison task, we recorded their brain activity using functional magnetic resonance imaging (fMRI). By applying a combination of representational similarity and fMRI-adaptation analyses, we found evidence of (i) a grid-like code, in the right postero-medial entorhinal cortex, representing the relative angular positions of words in the word space, and (ii) a distance-dependent code, in medial prefrontal, orbitofrontal, and mid-cingulate cortices, representing the Euclidean distance between words. Additionally, we found evidence that the brain also separately represents the single dimensions of word meaning: their implied size, encoded in visual areas, and their implied sound, in Heschl's gyrus/Insula. These results support the idea that the meaning of words, when they are organized along two dimensions, is represented in the human brain across multiple maps of different dimensionality. SIGNIFICANT STATEMENT: How do we represent the meaning of words and perform comparative judgements on them in our brain? According to influential theories, concepts are conceivable as points of an internal map (where distance represents similarity) that, as the physical space, can be mentally navigated. Here we use fMRI to show that when humans compare newly learnt words, they recruit a grid-like and a distance code, the same types of neural codes that, in mammals, represent relations between locations in the environment and support physical navigation between them.

Functional coupling between CA3 and laterobasal amygdala supports schema dependent memory formation.

The medial temporal lobe drives semantic congruence dependent memory formation. However, the exact roles of hippocampal subfields and surrounding brain regions remain unclear. Here, we used an established paradigm and high-resolution functional magnetic resonance imaging of the medial temporal lobe together with cytoarchitectonic probability estimates in healthy humans. Behaviorally, robust congruence effects emerged in young and older adults, indicating that schema dependent learning is unimpaired during healthy aging. Within the medial temporal lobe, semantic congruence was associated with hemodynamic activity in the subiculum, CA1, CA3 and dentate gyrus, as well as the entorhinal cortex and laterobasal amygdala. Importantly, a subsequent memory analysis showed increased activity for later remembered vs. later forgotten congruent items specifically within CA3, and this subfield showed enhanced functional connectivity to the laterobasal amygdala. As such, our findings extend current models on schema dependent learning by pinpointing the functional properties of subregions within the medial temporal lobe.

Partial volume correction in arterial spin labeling perfusion MRI: A method to disentangle anatomy from physiology or an analysis step too far?

The mismatch in the spatial resolution of Arterial Spin Labeling (ASL) MRI perfusion images and the anatomy of functionally distinct tissues in the brain leads to a partial volume effect (PVE), which in turn confounds the estimation of perfusion into a specific tissue of interest such as gray or white matter. This confound occurs because the image voxels contain a mixture of tissues with disparate perfusion properties, leading to estimated perfusion values that reflect primarily the volume proportions of tissues in the voxel rather than the perfusion of any particular tissue of interest within that volume. It is already recognized that PVE influences studies of brain perfusion, and that its effect might be even more evident in studies where changes in perfusion are co-incident with alterations in brain structure, such as studies involving a comparison between an atrophic patient population vs control subjects, or studies comparing subjects over a wide range of ages. However, the application of PVE correction (PVEc) is currently limited and the employed methodologies remain inconsistent. In this article, we outline the influence of PVE in ASL measurements of perfusion, explain the main principles of PVEc, and provide a critique of the current state of the art for the use of such methods. Furthermore, we examine the current use of PVEc in perfusion studies and whether there is evidence to support its wider adoption. We conclude that there is sound theoretical motivation for the use of PVEc alongside conventional, 'uncorrected', images, and encourage such combined reporting. Methods for PVEc are now available within standard neuroimaging toolboxes, which makes our recommendation straightforward to implement. However, there is still more work to be done to establish the value of PVEc as well as the efficacy and robustness of existing PVEc methods.

Robust parametric modeling of Alzheimer's disease progression.

Quantitative characterization of disease progression using longitudinal data can provide long-term predictions for the pathological stages of individuals. This work studies the robust modeling of Alzheimer's disease progression using parametric methods. The proposed method linearly maps the individual's age to a disease progression score (DPS) and jointly fits constrained generalized logistic functions to the longitudinal dynamics of biomarkers as functions of the DPS using M-estimation. Robustness of the estimates is quantified using bootstrapping via Monte Carlo resampling, and the estimated inflection points of the fitted functions are used to temporally order the modeled biomarkers in the disease course. Kernel density estimation is applied to the obtained DPSs for clinical status classification using a Bayesian classifier. Different M-estimators and logistic functions, including a novel type proposed in this study, called modified Stannard, are evaluated on the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for robust modeling of volumetric magnetic resonance imaging (MRI) and positron emission tomography (PET) biomarkers, cerebrospinal fluid (CSF) measurements, as well as cognitive tests. The results show that the modified Stannard function fitted using the logistic loss achieves the best modeling performance with an average normalized mean absolute error (NMAE) of 0.991 across all biomarkers and bootstraps. Applied to the ADNI test set, this model achieves a multiclass area under the ROC curve (AUC) of 0.934 in clinical status classification. The obtained results for the proposed model outperform almost all state-of-the-art results in predicting biomarker values and classifying clinical status. Finally, the experiments show that the proposed model, trained using abundant ADNI data, generalizes well to data from the National Alzheimer's Coordinating Center (NACC) with an average NMAE of 1.182 and a multiclass AUC of 0.929.

A probabilistic atlas of locus coeruleus pathways to transentorhinal cortex for connectome imaging in Alzheimer's disease.

According to the latest Braak staging of Alzheimer's disease (AD), tau pathology occurs earliest in the brain in the locus coeruleus (LC) of the brainstem, then propagates to the transentorhinal cortex (TEC), and later to other neocortical regions. Recent animal and in vivo human brain imaging research also support the trans-axonal propagation of tau pathology. In addition, neurochemical studies link norepinephrine to behavioral symptoms in AD. It is thus critical to examine the integrity of the LC-TEC pathway in studying the early development of the disease, but there has been limited work in this direction. By leveraging the high-resolution and multi-shell diffusion MRI data from the Human Connectome Project (HCP), in this work we develop a novel method for the reconstruction of the LC-TEC pathway in a cohort of 40 HCP subjects carefully selected based on rigorous quality control of the residual distortion artifacts in the brainstem. A probabilistic atlas of the LC-TEC pathway of both hemispheres is then developed in the MNI152 space and distributed publicly on the NITRC website. To apply our atlas on clinical imaging data, we develop an automated approach to calculate the medial core of the LC-TEC pathway for localized analysis of connectivity changes. In a cohort of 138 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we demonstrate the detection of the decreased fiber integrity in the LC-TEC pathways with increasing disease severity.