Lifelong olfactory deprivation-dependent cortical reorganization restricted to orbitofrontal cortex.

Prolonged sensory deprivation has repeatedly been linked to cortical reorganization. We recently demonstrated that individuals with congenital anosmia (CA, complete olfactory deprivation since birth) have seemingly normal morphology in piriform (olfactory) cortex despite profound morphological deviations in the orbitofrontal cortex (OFC), a finding contradictory to both the known effects of blindness on visual cortex and to the sparse literature on brain morphology in anosmia. To establish whether these unexpected findings reflect the true brain morphology in CA, we first performed a direct replication of our previous study to determine if lack of results was due to a deviant control group, a confound in cross sectional studies. Individuals with CA (n = 30) were compared to age and sex matched controls (n = 30) using voxel- and surface-based morphometry. The replication results were near identical to the original study: bilateral clusters of group differences in the OFC, including CA atrophy around the olfactory sulci and volume increases in the medial orbital gyri. Importantly, no group differences in piriform cortex were detected. Subsequently, to assess any subtle patterns of group differences not detectable by our mass-univariate analysis, we explored the data from a multivariate perspective. Combining the newly collected data with data from the replicated study (CA = 49, control = 49), we performed support vector machine classification based on gray matter volume. In line with the mass-univariate analyses, the multivariate analysis could accurately differentiate between the groups in bilateral OFC, whereas the classification accuracy in piriform cortex was at chance level. Our results suggest that despite lifelong olfactory deprivation, piriform (olfactory) cortex is morphologically unaltered and the morphological deviations in CA are confined to the OFC.

Differential response to donepezil in MRI subtypes of mild cognitive impairment.

BACKGROUND: Donepezil is an approved therapy for the treatment of Alzheimer's disease (AD). Results across clinical trials have been inconsistent, which may be explained by design-methodological issues, the pathophysiological heterogeneity of AD, and diversity of included study participants. We investigated whether response to donepezil differs in mild cognitive impaired (MCI) individuals demonstrating different magnetic resonance imaging (MRI) subtypes. METHODS: From the Hippocampus Study double-blind, randomized clinical trial, we included 173 MCI individuals (donepezil = 83; placebo = 90) with structural MRI data, at baseline and at clinical follow-up assessments (6-12-month). Efficacy outcomes were the annualized percentage change (APC) in hippocampal, ventricular, and total grey matter volumes, as well as in the AD cortical thickness signature. Participants were classified into MRI subtypes as typical AD, limbic-predominant, hippocampal-sparing, or minimal atrophy at baseline. We primarily applied a subtyping approach based on continuous scale of two subtyping dimensions. We also used the conventional categorical subtyping approach for comparison. RESULTS: Donepezil-treated MCI individuals showed slower atrophy rates compared to the placebo group, but only if they belonged to the minimal atrophy or hippocampal-sparing subtypes. Importantly, only the continuous subtyping approach, but not the conventional categorical approach, captured this differential response. CONCLUSIONS: Our data suggest that individuals with MCI, with hippocampal-sparing or minimal atrophy subtype, may have improved benefit from donepezil, as compared with MCI individuals with typical or limbic-predominant patterns of atrophy. The newly proposed continuous subtyping approach may have advantages compared to the conventional categorical approach. Future research is warranted to demonstrate the potential of subtype stratification for disease prognosis and response to treatment. TRIAL REGISTRATION: ClinicalTrial.gov NCT00403520. Submission Date: November 21, 2006.

A deep learning model for brain age prediction using minimally preprocessed T1w images as input.

Introduction: In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses one T1w MRI preprocessing step when applying the model to external datasets to simplify implementation and increase accessibility in research settings. Our model only requires rigid image registration to the MNI space, which is an advantage compared to previous methods that require more preprocessing steps, such as feature extraction. Methods: We used a multicohort dataset of cognitively healthy individuals (age range = 32.0-95.7 years) comprising 17,296 MRIs for training and evaluation. We compared our model using hold-out (CNN1) and cross-validation (CNN2-4) approaches. To verify generalisability, we used two external datasets with different populations and MRI scan characteristics to evaluate the model. To demonstrate its usability, we included the external dataset's images in the cross-validation training (CNN3). To ensure that our model used only the brain signal on the image, we also predicted brain age using skull-stripped images (CNN4). Results: The trained models achieved a mean absolute error of 2.99, 2.67, 2.67, and 3.08 years for CNN1-4, respectively. The model's performance in the external dataset was in the typical range of mean absolute error (MAE) found in the literature for testing sets. Adding the external dataset to the training set (CNN3), overall, MAE is unaffected, but individual cohort MAE improves (5.63-2.25 years). Salience maps of predictions reveal that periventricular, temporal, and insular regions are the most important for age prediction. Discussion: We provide indicators for using biological (predicted) brain age as a metric for age correction in neuroimaging studies as an alternative to the traditional chronological age. In conclusion, using different approaches, our CNN-based model showed good performance using one T1w brain MRI preprocessing step. The proposed CNN model is made publicly available for the research community to be easily implemented and used to study ageing and age-related disorders.

Inter-modality assessment of medial temporal lobe atrophy in a non-demented population: application of a visual rating scale template across radiologists with varying clinical experience.

OBJECTIVES: To assess inter-modality agreement and accuracy for medial temporal lobe atrophy (MTA) ratings across radiologists with varying clinical experience in a non-demented population. METHODS: Four raters (two junior radiologists and two senior neuroradiologists) rated MTA on CT and MRI scans using Scheltens' MTA scale. Ratings were compared to a consensus rating by two experienced neuroradiologists for estimation of true positive and negative rates (TPR and TNR) and over- and underestimation of MTA. Inter-modality agreement expressed as Cohen's κ (dichotomized data), Cohen's κw, and two-way mixed, single measures, consistency ICC (ordinal data) were determined. Adequate agreement was defined as κ/κw ≥ 0.80 and ICC ≥ 0.80 (significance level at 95% CI ≥ 0.65). RESULTS: Forty-nine subjects (median age 72 years, 27% abnormal MTA) with cognitive impairment were included. Only junior radiologists achieved adequate agreement expressed as Cohen's κ. All raters achieved adequate agreement expressed as Cohen's κw and ICC. True positive rates varied from 69 to 100% and TNR varied from 85 to 100%. No under- or overestimation of MTA was observed. Ratings did not differ between radiologists. CONCLUSION: We conclude that radiologists with varying experience achieve adequate inter-modality agreement and similar accuracy when Scheltens' MTA scale is used to rate MTA on a non-demented population. However, TPR varied between radiologists which could be attributed to rating style differences. KEY POINTS: • Radiologists with varying experience achieve adequate inter-modality agreement with similar accuracy when Scheltens' MTA scale is used to rate MTA on a non-demented population. • Differences in rating styles might affect accuracy, this was most evident for senior neuroradiologists, and only junior radiologists achieved adequate agreement on dichotomized (abnormal/normal) ratings. • The use of an MTA scale template might compensate for varying clinical experience which could make it applicable for clinical use.

Seeing Beyond Your Nose? The Effects of Lifelong Olfactory Sensory Deprivation on Cerebral Audio-visual Integration.

Lifelong auditory and visual sensory deprivation have been demonstrated to alter both perceptual acuity and the neural processing of remaining senses. Recently, it was demonstrated that individuals with anosmia, i.e. complete olfactory sensory deprivation, displayed enhanced multisensory integration performance. Whether this ability is due to a reorganization of olfactory processing regions to focus on cross-modal multisensory information or whether it is due to enhanced processing within multisensory integration regions is not known. To dissociate these two outcomes, we investigated the neural processing of dynamic audio-visual stimuli in individuals with congenital anosmia and matched controls (both groups, n = 33) using functional magnetic resonance imaging. Specifically, we assessed whether the previously demonstrated multisensory enhancement is related to cross-modal processing of multisensory stimuli in olfactory associated regions, the piriform and olfactory orbitofrontal cortices, or enhanced multisensory processing in established multisensory integration regions, the superior temporal and intraparietal sulci. No significant group differences were found in the a priori hypothesized regions using region of interest analyses. However, exploratory whole-brain analysis suggested higher activation related to multisensory integration within the posterior superior temporal sulcus, in close proximity to the multisensory region of interest, in individuals with congenital anosmia. No group differences were demonstrated in olfactory associated regions. Although results were outside our hypothesized regions, combined, they tentatively suggest that enhanced processing of audio-visual stimuli in individuals with congenital anosmia may be mediated by multisensory, and not primary sensory, cerebral regions.

Inter-Cohort Validation of SuStaIn Model for Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder which spans several years from preclinical manifestations to dementia. In recent years, interest in the application of machine learning (ML) algorithms to personalized medicine has grown considerably, and a major challenge that such models face is the transferability from the research settings to clinical practice. The objective of this work was to demonstrate the transferability of the Subtype and Stage Inference (SuStaIn) model from well-characterized research data set, employed as training set, to independent less-structured and heterogeneous test sets representative of the clinical setting. The training set was composed of MRI data of 1043 subjects from the Alzheimer's disease Neuroimaging Initiative (ADNI), and the test set was composed of data from 767 subjects from OASIS, Pharma-Cog, and ViTA clinical datasets. Both sets included subjects covering the entire spectrum of AD, and for both sets volumes of relevant brain regions were derived from T1-3D MRI scans processed with Freesurfer v5.3 cross-sectional stream. In order to assess the predictive value of the model, subpopulations of subjects with stable mild cognitive impairment (MCI) and MCIs that progressed to AD dementia (pMCI) were identified in both sets. SuStaIn identified three disease subtypes, of which the most prevalent corresponded to the typical atrophy pattern of AD. The other SuStaIn subtypes exhibited similarities with the previously defined hippocampal sparing and limbic predominant atrophy patterns of AD. Subject subtyping proved to be consistent in time for all cohorts and the staging provided by the model was correlated with cognitive performance. Classification of subjects on the basis of a combination of SuStaIn subtype and stage, mini mental state examination and amyloid-ß1-42 cerebrospinal fluid concentration was proven to predict conversion from MCI to AD dementia on par with other novel statistical algorithms, with ROC curves that were not statistically different for the training and test sets and with area under curve respectively equal to 0.77 and 0.76. This study proves the transferability of a SuStaIn model for AD from research data to less-structured clinical cohorts, and indicates transferability to the clinical setting.

Normal Olfactory Functional Connectivity Despite Lifelong Absence of Olfactory Experiences.

Congenital blindness is associated with atypical morphology and functional connectivity within and from visual cortical regions; changes that are hypothesized to originate from a lifelong absence of visual input and could be regarded as a general (re) organization principle of sensory cortices. Challenging this is the fact that individuals with congenital anosmia (lifelong olfactory sensory loss) display little to no morphological changes in the primary olfactory cortex. To determine whether olfactory input from birth is essential to establish and maintain normal functional connectivity in olfactory processing regions, akin to the visual system, we assessed differences in functional connectivity within the olfactory cortex between individuals with congenital anosmia (n = 33) and matched controls (n = 33). Specifically, we assessed differences in connectivity between core olfactory processing regions as well as differences in regional homogeneity and homotopic connectivity within the primary olfactory cortex. In contrast to congenital blindness, none of the analyses indicated atypical connectivity in individuals with congenital anosmia. In fact, post-hoc Bayesian analysis provided support for an absence of group differences. These results suggest that a lifelong absence of olfactory experience has a limited impact on the functional connectivity in the olfactory cortex, a finding that indicates a clear difference between sensory modalities in how sensory cortical regions develop.

Comparison of subtyping methods for neuroimaging studies in Alzheimer's disease: a call for harmonization.

Biological subtypes in Alzheimer's disease, originally identified on neuropathological data, have been translated to in vivo biomarkers such as structural magnetic resonance imaging and positron emission tomography, to disentangle the heterogeneity within Alzheimer's disease. Although there is methodological variability across studies, comparable characteristics of subtypes are reported at the group level. In this study, we investigated whether group-level similarities translate to individual-level agreement across subtyping methods, in a head-to-head context. We compared five previously published subtyping methods. Firstly, we validated the subtyping methods in 89 amyloid-beta positive Alzheimer's disease dementia patients (reference group: 70 amyloid-beta negative healthy individuals) using structural magnetic resonance imaging. Secondly, we extended and applied the subtyping methods to 53 amyloid-beta positive prodromal Alzheimer's disease and 30 amyloid-beta positive Alzheimer's disease dementia patients (reference group: 200 amyloid-beta negative healthy individuals) using structural magnetic resonance imaging and tau positron emission tomography. Subtyping methods were implemented as outlined in each original study. Group-level and individual-level comparisons across methods were performed. Each individual subtyping method was replicated, and the proof-of-concept was established. At the group level, all methods captured subtypes with similar patterns of demographic and clinical characteristics, and with similar cortical thinning and tau positron emission tomography uptake patterns. However, at the individual level, large disagreements were found in subtype assignments. Although characteristics of subtypes are comparable at the group level, there is a large disagreement at the individual level across subtyping methods. Therefore, there is an urgent need for consensus and harmonization across subtyping methods. We call for the establishment of an open benchmarking framework to overcome this problem.

The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study.

Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with images acquired with a single scanner or with strict protocol harmonization, which is not representative of a clinical setting. The aim of this study was to investigate how well a DL model performs in unseen clinical datasets-collected with different scanners, protocols and disease populations-and whether more heterogeneous training data improves generalization. In total, 3117 MRI scans of brains from multiple dementia research cohorts and memory clinics, that had been visually rated by a neuroradiologist according to Scheltens' scale of medial temporal atrophy (MTA), were included in this study. By training multiple versions of a convolutional neural network on different subsets of this data to predict MTA ratings, we assessed the impact of including images from a wider distribution during training had on performance in external memory clinic data. Our results showed that our model generalized well to datasets acquired with similar protocols as the training data, but substantially worse in clinical cohorts with visibly different tissue contrasts in the images. This implies that future DL studies investigating performance in out-of-distribution (OOD) MRI data need to assess multiple external cohorts for reliable results. Further, by including data from a wider range of scanners and protocols the performance improved in OOD data, which suggests that more heterogeneous training data makes the model generalize better. To conclude, this is the most comprehensive study to date investigating the domain shift in deep learning on MRI data, and we advocate rigorous evaluation of DL models on clinical data prior to being certified for deployment.

Medial temporal atrophy in preclinical dementia: Visual and automated assessment during six year follow-up.

Medial temporal lobe (MTL) atrophy is an important morphological marker of many dementias and is closely related to cognitive decline. In this study we aimed to characterize longitudinal progression of MTL atrophy in 93 individuals with subjective cognitive decline and mild cognitive impairment followed up over six years, and to assess if clinical rating scales are able to detect these changes. All MRI images were visually rated according to Scheltens' scale of medial temporal atrophy (MTA) by two neuroradiologists and AVRA, a software for automated MTA ratings. The images were also segmented using FreeSurfer's longitudinal pipeline in order to compare the MTA ratings to volumes of the hippocampi and inferior lateral ventricles. We found that MTL atrophy rates increased with CSF biomarker abnormality, used to define preclinical stages of Alzheimer's Disease. Both AVRA's and the radiologists' MTA ratings showed similar longitudinal trends as the subcortical volumes, suggesting that visual rating scales provide a valid alternative to automatic segmentations. Our results further showed that it took more than 8 years on average for individuals with mild cognitive impairment, and an Alzheimer's disease biomarker profile, to increase the MTA score by one. This suggests that discrete MTA ratings are too coarse for tracking individual MTL atrophy in short time spans. While the MTA scores from each radiologist showed strong correlations to subcortical volumes, the inter-rater agreement was low. We conclude that the main limitation of quantifying MTL atrophy with visual ratings in clinics is the subjectiveness of the assessment.

Morphological changes in secondary, but not primary, sensory cortex in individuals with life-long olfactory sensory deprivation.

Individuals with congenital sensory deprivation usually demonstrate altered brain morphology in areas associated with early processing of the absent sense. Here, we aimed to establish whether this also applies to individuals born without a sense of smell (congenital anosmia) by comparing cerebral morphology between 33 individuals with isolated congenital anosmia and matched controls. We detected no morphological alterations in the primary olfactory (piriform) cortex. However, individuals with anosmia demonstrated gray matter volume atrophy in bilateral olfactory sulci, explained by decreased cortical area, curvature, and sulcus depth. They further demonstrated increased gray matter volume and cortical thickness in the medial orbital gyri; regions closely associated with olfactory processing, sensory integration, and value-coding. Our results suggest that a lifelong absence of sensory input does not necessarily lead to morphological alterations in primary sensory cortex and extend previous findings with divergent morphological alterations in bilateral orbitofrontal cortex, indicating influences of different developmental processes.

AVRA: Automatic visual ratings of atrophy from MRI images using recurrent convolutional neural networks.

Quantifying the degree of atrophy is done clinically by neuroradiologists following established visual rating scales. For these assessments to be reliable the rater requires substantial training and experience, and even then the rating agreement between two radiologists is not perfect. We have developed a model we call AVRA (Automatic Visual Ratings of Atrophy) based on machine learning methods and trained on 2350 visual ratings made by an experienced neuroradiologist. It provides fast and automatic ratings for Scheltens' scale of medial temporal atrophy (MTA), the frontal subscale of Pasquier's Global Cortical Atrophy (GCA-F) scale, and Koedam's scale of Posterior Atrophy (PA). We demonstrate substantial inter-rater agreement between AVRA's and a neuroradiologist ratings with Cohen's weighted kappa values of κw = 0.74/0.72 (MTA left/right), κw = 0.62 (GCA-F) and κw = 0.74 (PA). We conclude that automatic visual ratings of atrophy can potentially have great scientific value, and aim to present AVRA as a freely available toolbox.

Stability of graph theoretical measures in structural brain networks in Alzheimer's disease.

Graph analysis has become a popular approach to study structural brain networks in neurodegenerative disorders such as Alzheimer's disease (AD). However, reported results across similar studies are often not consistent. In this paper we investigated the stability of the graph analysis measures clustering, path length, global efficiency and transitivity in a cohort of AD (N = 293) and control subjects (N = 293). More specifically, we studied the effect that group size and composition, choice of neuroanatomical atlas, and choice of cortical measure (thickness or volume) have on binary and weighted network properties and relate them to the magnitude of the differences between groups of AD and control subjects. Our results showed that specific group composition heavily influenced the network properties, particularly for groups with less than 150 subjects. Weighted measures generally required fewer subjects to stabilize and all assessed measures showed robust significant differences, consistent across atlases and cortical measures. However, all these measures were driven by the average correlation strength, which implies a limitation of capturing more complex features in weighted networks. In binary graphs, significant differences were only found in the global efficiency and transitivity measures when using cortical thickness measures to define edges. The findings were consistent across the two atlases, but no differences were found when using cortical volumes. Our findings merits future investigations of weighted brain networks and suggest that cortical thickness measures should be preferred in future AD studies if using binary networks. Further, studying cortical networks in small cohorts should be complemented by analyzing smaller, subsampled groups to reduce the risk that findings are spurious.

Atrophy of the Posterior Subiculum Is Associated with Memory Impairment, Tau- and Aß Pathology in Non-demented Individuals.

Alzheimer's disease (AD) is associated with atrophy of the cornu ammonis (CA) 1 and the subiculum subfield of the hippocampus (HC), and with deficits in episodic memory and spatial orientation. These deficits are mainly associated with the functionality of the posterior HC. We therefore hypothesized that key AD pathologies, i.e., ß-amyloid and tau pathology would be particularly associated with the volume of the posterior subiculum in non-demented individuals. In our study we included 302 cognitively normal elderly participants (CN), 183 patients with subjective cognitive decline (SCD) and 171 patients with amnestic mild cognitive impairment (MCI), all of whom underwent 3T magnetic resonance images (MRI). The subicular subfield was segmented using Freesurfer 5.3 and divided into 10 volumetric segments moving from the most posterior (segment 1) to the most anterior part along the axis of the hippocampal head and body (segment 10). Cerebrospinal fluid (CSF) Aß42 and phosphorylated tau (P-tau) were quantified using ELISA and were used as biomarkers for ß-amyloid and tau pathology, respectively. In the total sample, tau-pathology and Aß-pathology and (measured by elevated P-tau and low Aß42 levels in CSF) and mild memory dysfunction were mostly associated with the volume changes of the posterior subiculum. Both SCD and MCI patients with elevated P-tau or low Aß42 levels displayed predominantly posterior subicular atrophy in comparisons to control subjects with normal CSF biomarker levels. Finally, there was no main effect of Aß42 or P-tau when comparing SCD with abnormal P-tau or Aß42 with SCD with normal levels of these CSF-biomarkers. However, in the left subiculum there was a significant interaction revealing atrophy in the left posterior but not the anterior subiculum in participants with low Aß42 levels. The same pattern was observed on the contralateral side in participants with elevated P-tau levels. In conclusion, AD pathologies and mild memory dysfunction are mainly associated with atrophy of the posterior parts of the subicular subfields of the HC in non-demented individuals. In light of these findings we suggest that segmentation of the HC subfields may benefit from considering the volume of the different anterior-posterior subsections of each subfield.