Locus coeruleus contrast and diffusivity metrics differentially relate to age and memory performance.

Neurocognitive aging researchers are increasingly focused on the locus coeruleus, a neuromodulatory brainstem structure that degrades with age. With this rapid growth, the field will benefit from consensus regarding which magnetic resonance imaging (MRI) metrics of locus coeruleus structure are most sensitive to age and cognition. To address this need, the current study acquired magnetization transfer- and diffusion-weighted MRI images in younger and older adults who also completed a free recall memory task. Results revealed significantly larger differences between younger and older adults for maximum than average magnetization transfer-weighted contrast (MTC), axial than mean or radial single-tensor diffusivity (DTI), and free than restricted multi-compartment diffusion (NODDI) metrics in the locus coeruleus; with maximum MTC being the best predictor of age group. Age effects for all imaging modalities interacted with sex, with larger age group differences in males than females for MTC and NODDI metrics. Age group differences also varied across locus coeruleus subdivision for DTI and NODDI metrics, and across locus coeruleus hemispheres for MTC. Within older adults, however, there were no significant effects of age on MTC or DTI metrics, only an interaction between age and sex for free diffusion. Finally, independent of age and sex, higher restricted diffusion in the locus coeruleus was significantly related to better (lower) recall variability, but not mean recall. Whereas MTC has been widely used in the literature, our comparison between the average and maximum MTC metrics, inclusion of DTI and NODDI metrics, and breakdowns by locus coeruleus subdivision and hemisphere make important and novel contributions to our understanding of the aging of locus coeruleus structure.

Examining iron-related off-target binding effects of 18F-AV1451 PET in the cortex of Aß+ individuals.

The presence of neurofibrillary tangles containing hyper-phosphorylated tau is a characteristic of Alzheimer's disease (AD) pathology. The positron emission tomography (PET) radioligand sensitive to tau neurofibrillary tangles (18F-AV1451) also binds with iron. This off-target binding effect may be enhanced in older adults on the AD spectrum, particularly those with amyloid-positive biomarkers. Here, we examined group differences in 18F-AV1451 PET after controlling for iron-sensitive measures from magnetic resonance imaging (MRI) and its relationships to tissue microstructure and cognition in 40 amyloid beta positive (Aß+) individuals, 20 amyloid beta negative (Aß-) with MCI and 31 Aß- control participants. After controlling for iron, increased 18F-AV1451 PET uptake was found in the temporal lobe and hippocampus of Aß+ participants compared to Aß- MCI and control participants. Within the Aß+ group, significant correlations were seen between 18F-AV1451 PET uptake and tissue microstructure and these correlations remained significant after controlling for iron. These findings indicate that off-target binding of iron to the 18F-AV1451 ligand may not affect its sensitivity to Aß status or cognition in early-stage AD.

Locus coeruleus integrity predicts ease of attaining and maintaining neural states of high attentiveness.

The locus coeruleus (LC), a small subcortical structure in the brainstem, is the brain's principal source of norepinephrine. It plays a primary role in regulating stress, the sleep-wake cycle, and attention, and its degradation is associated with aging and neurodegenerative diseases associated with cognitive deficits (e.g., Parkinson's, Alzheimer's). Yet precisely how norepinephrine drives brain networks to support healthy cognitive function remains poorly understood - partly because LC's small size makes it difficult to study noninvasively in humans. Here, we characterized LC's influence on brain dynamics using a hidden Markov model fitted to functional neuroimaging data from healthy young adults across four attention-related brain networks and LC. We modulated LC activity using a behavioral paradigm and measured individual differences in LC magnetization transfer contrast. The model revealed five hidden states, including a stable state dominated by salience-network activity that occurred when subjects actively engaged with the task. LC magnetization transfer contrast correlated with this state's stability across experimental manipulations and with subjects' propensity to enter into and remain in this state. These results provide new insight into LC's role in driving spatiotemporal neural patterns associated with attention, and demonstrate that variation in LC integrity can explain individual differences in these patterns even in healthy young adults.

Nigral volume loss in prodromal, early, and moderate Parkinson's disease.

The loss of melanized neurons in the substantia nigra pars compacta (SNc) is a hallmark pathology in Parkinson's disease (PD). Melanized neurons in SNc can be visualized in vivo using magnetization transfer (MT) effects. Nigral volume was extracted in data acquired with a MT-prepared gradient echo sequence in 33 controls, 83 non-manifest carriers (42 LRRK2 and 41 GBA nonmanifest carriers), 65 prodromal hyposmic participants, 105 de novo PD patients and 26 48-month PD patients from the Parkinson's Progressive Markers Initiative. No difference in nigral volume was seen between controls and LRRK2 and GBA non-manifest carriers (F=0.076; P=0.927). A significant main effect in group was observed between controls, prodromal hyposmic participants, and overt PD patients (F=5.192; P=0.002). Longer disease duration significantly correlated with lower nigral volume (r=-0.252; P=0.010). This study shows that nigral depigmentation can be robustly detected in prodromal hyposmic participants and overt PD patients.

In vivo detection of substantia nigra and locus coeruleus volume loss in Parkinson's disease using neuromelanin-sensitive MRI: Replication in two cohorts.

Patients with Parkinson's disease undergo a loss of melanized neurons in substantia nigra pars compacta and locus coeruleus. Very few studies have assessed substantia nigra pars compacta and locus coeruleus pathology in Parkinson's disease simultaneously with magnetic resonance imaging (MRI). Neuromelanin-sensitive MRI measures of substantia nigra pars compacta and locus coeruleus volume based on explicit magnetization transfer contrast have been shown to have high scan-rescan reproducibility in controls, but no study has replicated detection of Parkinson's disease-associated volume loss in substantia nigra pars compacta and locus coeruleus in multiple cohorts with the same methodology. Two separate cohorts of Parkinson's disease patients and controls were recruited from the Emory Movement Disorders Clinic and scanned on two different MRI scanners. In cohort 1, imaging data from 19 controls and 22 Parkinson's disease patients were acquired with a Siemens Trio 3 Tesla scanner using a 2D gradient echo sequence with magnetization transfer preparation pulse. Cohort 2 consisted of 33 controls and 39 Parkinson's disease patients who were scanned on a Siemens Prisma 3 Tesla scanner with a similar imaging protocol. Locus coeruleus and substantia nigra pars compacta volumes were segmented in both cohorts. Substantia nigra pars compacta volume (Cohort 1: p = 0.0148; Cohort 2: p = 0.0011) and locus coeruleus volume (Cohort 1: p = 0.0412; Cohort 2: p = 0.0056) were significantly reduced in the Parkinson's disease group as compared to controls in both cohorts. This imaging approach robustly detects Parkinson's disease effects on these structures, indicating that it is a promising marker for neurodegenerative neuromelanin loss.

A Novel Hidden Markov Approach to Studying Dynamic Functional Connectivity States in Human Neuroimaging.

Introduction: Hidden Markov models (HMMs) are a popular choice to extract and examine recurring patterns of activity or functional connectivity in neuroimaging data, both in terms of spatial patterns and their temporal progression. Although many diverse HMMs have been applied to neuroimaging data, most have defined states based on activity levels (intensity-based [IB] states) rather than patterns of functional connectivity between brain areas (connectivity-based states), which is problematic if we want to understand connectivity dynamics: IB states are unlikely to provide comprehensive information about dynamic connectivity patterns. Methods: We addressed this problem by introducing a new HMM that defines states based on full functional connectivity (FFC) profiles among brain regions. We empirically explored the behavior of this new model in comparison to existing approaches based on IB or summed functional connectivity states using the Human Connectome Project unrelated 100 functional magnetic resonance imaging "resting-state" dataset. Results: Our FFC model discovered connectivity states with more distinguishable (i.e., unique and separable from each other) patterns than previous approaches, and recovered simulated connectivity-based states more faithfully than the other models tested. Discussion: Thus, if our goal is to extract and interpret connectivity states in neuroimaging data, our new model outperforms previous methods, which miss crucial information about the evolution of functional connectivity in the brain. Impact statement Hidden Markov models (HMMs) can be used to investigate brain states noninvasively. Previous models "recover" connectivity from intensity-based hidden states, or from connectivity "summed" across nodes. In this study, we introduce a novel connectivity-based HMM and show how it can reveal true connectivity hidden states under minimal assumptions.

Nigral volumetric and microstructural measures in individuals with scans without evidence of dopaminergic deficit.

Introduction: Striatal dopamine transporter (DAT) imaging using 123I-ioflupane single photon positron emitted computed tomography (SPECT) (DaTScan, GE) identifies 5-20% of newly diagnosed Parkinson's disease (PD) subjects enrolling in clinical studies to have scans without evidence of dopaminergic deficit (SWEDD). These individuals meet diagnostic criteria for PD, but do not clinically progress as expected, and they are not believed to have neurodegenerative Parkinsonism. Inclusion of SWEDD participants in PD biomarker studies or therapeutic trials may therefore cause them to fail. DaTScan can identify SWEDD individuals, but it is expensive and not widely available; an alternative imaging approach is needed. Here, we evaluate the use of neuromelanin-sensitive, iron-sensitive, and diffusion contrasts in substantia nigra pars compacta (SNpc) to differentiate SWEDD from PD individuals. Methods: Neuromelanin-sensitive, iron-sensitive, and diffusion imaging data for SWEDD, PD, and control subjects were downloaded from the Parkinson's progression markers initiative (PPMI) database. SNpc volume, SNpc iron (R 2), and SNpc free water (FW) were measured for each participant. Results: Significantly smaller SNpc volume was seen in PD as compared to SWEDD (P < 10-3) and control (P < 10-3) subjects. SNpc FW was elevated in the PD group relative to controls (P = 0.017). No group difference was observed in SNpc R 2. Conclusion: In conclusion, nigral volume and FW in the SWEDD group were similar to that of controls, while a reduction in nigral volume and increased FW were observed in the PD group relative to SWEDD and control participants. These results suggest that these MRI measures should be explored as a cost-effective alternative to DaTScan for evaluation of the nigrostriatal system.

Subtyping Autism Spectrum Disorder Via Joint Modeling of Clinical and Connectomic Profiles.

Background: Autism spectrum disorder (ASD) is a highly heterogeneous developmental disorder with diverse clinical manifestations. Neuroimaging studies have explored functional connectivity (FC) of ASD through resting-state functional magnetic resonance imaging studies; however, the findings have remained inconsistent, thus reflecting the possibility of multiple subtypes. Identification of the relationship between clinical symptoms and FC measures may help clarify the inconsistencies in earlier findings and advance our understanding of ASD subtypes. Methods: Canonical correlation analysis was performed on 210 ASD subjects from the Autism Brain Imaging Data Exchange to identify significant linear combinations of resting-state connectomic and clinical profiles of ASD. Then, hierarchical clustering defined ASD subtypes based on distinct brain-behavior relationships. Finally, a support vector machine (SVM) classifier was used to verify that subtypes comprised subjects with distinct clinical and connectivity features. Results: Three ASD subtypes were identified. Subtype 1 exhibited increased intra-network FC, increased Intelligence Quotient (IQ) scores, and restricted and repetitive behaviors. Subtype 2 was characterized by decreased whole-brain FC and more severe Autism Diagnostic Interview-Revised and Social Responsiveness Scale symptoms. Subtype 3 demonstrated mixed FC, low IQ scores, as well as social motivation and verbal deficits. To verify subtype assignment, a multi-class SVM using connectomic and clinical profiles yielded an average accuracy of 71.3% and 65.2% respectively for subtype classification, which is significantly higher than chance (33.3%). Conclusion: The present study demonstrates that combining connectomic and behavioral measures is a powerful approach for disease subtyping and suggests that there are ASD subtypes with distinct connectomic and clinical profiles.

Impact of Locus Coeruleus and Its Projections on Memory and Aging.

Introduction: Locus coeruleus (LC) is the primary source of norepinephrine to the brain and its efferent projections innervate many brain regions, including the thalamus. The LC degrades with normal aging, but not much is known regarding whether its structural connectivity evolves with age or predicts aspects of cognition. Methods: Here, we use high-resolution diffusion tensor imaging-based tractography to examine structural connectivity between LC and the thalamus in younger and older adults. Results: We found LC projections to be bundled in a fiber tract anatomically consistent with the central tegmental tract (CTT) and branched from this tract into the thalamus. The older cohort exhibited a significant reduction in mean and radial diffusivity within CTT, as compared with the young cohort. We also observed a significant correlation between CTT mean, axial, and radial diffusivities and memory performance (delayed recall) in the older adult cohort. Discussion: These observations suggest that although LC projections degrade with age, the degree of degradation is associated with cognitive abilities in older adults. Impact statement Locus coeruleus (LC) modulates several cognitive processes, including modulating arousal, attention modulation, and memory. Sustaining the integrity of LC neurons is hypothesized to play a key role in staving off age-related cognitive decline. However, less is known about how efferent projections of LC change with age or cognition. Here, we examine how age affects the microstructure of the central tegmental tract, a fiber tract in which LC efferent projections are bundled, and whether age-related changes in the microstructure of this tract are associated with cognitive decline.

Improving Between-Group Effect Size for Multi-Site Functional Connectivity Data via Site-Wise De-Meaning.

Background: Multi-site functional MRI (fMRI) databases are becoming increasingly prevalent in the study of neurodevelopmental and psychiatric disorders. However, multi-site databases are known to introduce site effects that may confound neurobiological and measures such as functional connectivity (FC). Although studies have been conducted to mitigate site effects, these methods often result in reduced effect size in FC comparisons between controls and patients. Methods: We present a site-wise de-meaning (SWD) strategy in multi-site FC analysis and compare its performance with two common site-effect mitigation methods, i.e., generalized linear model (GLM) and Combining Batches (ComBat) Harmonization. For SWD, after FC was calculated and Fisher z-transformed, the site-wise FC mean was removed from each subject before group-level statistical analysis. The above methods were tested on two multi-site psychiatric consortiums [Autism Brain Imaging Data Exchange (ABIDE) and Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP)]. Preservation of consistent FC alterations in patients were evaluated for each method through the effect sizes (Hedge's g) of patients vs. controls. Results: For the B-SNIP dataset, SWD improved the effect size between schizophrenic and control subjects by 4.5-7.9%, while GLM and ComBat decreased the effect size by 22.5-42.6%. For the ABIDE dataset, SWD improved the effect size between autistic and control subjects by 2.9-5.3%, while GLM and ComBat decreased the effect size by up to 11.4%. Conclusion: Compared to the original data and commonly used methods, the SWD method demonstrated superior performance in preserving the effect size in FC features associated with disorders.

Graph convolutional network for fMRI analysis based on connectivity neighborhood.

There have been successful applications of deep learning to functional magnetic resonance imaging (fMRI), where fMRI data were mostly considered to be structured grids, and spatial features from Euclidean neighbors were usually extracted by the convolutional neural networks (CNNs) in the computer vision field. Recently, CNN has been extended to graph data and demonstrated superior performance. Here, we define graphs based on functional connectivity and present a connectivity-based graph convolutional network (cGCN) architecture for fMRI analysis. Such an approach allows us to extract spatial features from connectomic neighborhoods rather than from Euclidean ones, consistent with the functional organization of the brain. To evaluate the performance of cGCN, we applied it to two scenarios with resting-state fMRI data. One is individual identification of healthy participants and the other is classification of autistic patients from normal controls. Our results indicate that cGCN can effectively capture functional connectivity features in fMRI analysis for relevant applications.

Context-dependent dynamic functional connectivity alteration of lateral occipital cortex in schizophrenia.

Schizophrenia has long been associated with dysfunction in visual perception. One important region underlying this is lateral occipital cortex (LOC), a mid-level visual region critical for object recognition. Although LOC of patients has exhibited structural and functional abnormalities in MR brain imaging studies, how it interacts with other networks over time under rest and with task demands remains to be clarified. The present study investigated the spatial-temporal interaction of LOC with other brain networks by examining functional connectivity communities of the brain over time. We found increased temporal instability of LOC connectivity over time under both resting and task-switching conditions in patients. In the resting state, LOC of patients had increased interaction with the frontoparietal task-control network (FPTC) and thalamus compared with controls, while during task switching, LOC showed increased interaction with the default mode network (DMN). Temporal instability of LOC connectivity was positively correlated with patients' switching cost during task performance and with hallucination severity. These results indicate that reduced stability of LOC connectivity may be an important factor underlying neurocognitive dysfunctions and symptom severity in schizophrenia.

Application of Convolutional Recurrent Neural Network for Individual Recognition Based on Resting State fMRI Data.

In most task and resting state fMRI studies, a group consensus is often sought, where individual variability is considered a nuisance. None the less, biological variability is an important factor that cannot be ignored and is gaining more attention in the field. One recent development is the individual identification based on static functional connectome. While the original work was based on the static connectome, subsequent efforts using recurrent neural networks (RNN) demonstrated that the inclusion of temporal features greatly improved identification accuracy. Given that convolutional RNN (ConvRNN) seamlessly integrates spatial and temporal features, the present work applied ConvRNN for individual identification with resting state fMRI data. Our result demonstrates ConvRNN achieving a higher identification accuracy than conventional RNN, likely due to better extraction of local features between neighboring ROIs. Furthermore, given that each convolutional output assembles in-place features, they provide a natural way for us to visualize the informative spatial pattern and temporal information, opening up a promising new avenue for analyzing fMRI data.

Increased inflammation and brain glutamate define a subtype of depression with decreased regional homogeneity, impaired network integrity, and anhedonia.

Combined increases in peripheral inflammation and brain glutamate may identify a subtype of depression with distinct neuroimaging signatures. Two contrasting subgroups of depressed subjects-with and without combined elevations in plasma C-reactive protein (CRP) and basal ganglia glutamate (high and low CRP-Glu, respectively) were identified by hierarchical clustering using plasma CRP (indexing peripheral inflammation) and magnetic resonance spectroscopy (MRS)-based measurement of left basal ganglia glutamate. High CRP-Glu group status was associated with greater severity of anhedonia and cognitive and motor slowing. Local- and network-level measures of functional integrity were determined using brain oxygen level-dependent (BOLD)-oscillatory activity and graph theory. Greater decreases in concordance of oscillatory activity between neighboring voxels (Regional Homogeneity 'ReHo', p < 0.01) within the MRS volume-of-interest was associated with the High CRP-Glu subgroup. Using brain-wide, CRP-Glu ReHo contrast maps, a covariance network of 41 regions-of-interest (ROIs) with similar ReHo decreases was identified in the High CRP-Glu group and was located to brain structures previously implicated in depression. The 41-ROI network was further decomposed into four subnetworks. ReHo decreases within Subnetwork4-comprised of reward processing regions -was associated with anhedonia. Subnetwork4 ReHo also predicted decreased network integrity, which mediated the link between local ReHo and anhedonia in the Low but not High CRP-Glu group. These findings suggest that decreased ReHo and related disruptions in network integrity may reflect toxic effects of inflammation-induced increases in extrasynaptic glutamate signaling. Moreover, local BOLD oscillatory activity as reflected in ReHo might be a useful measure of target-engagement in the brain for treatment of inflammation-induced behaviors.

Neuromelanin detection by magnetic resonance imaging (MRI) and its promise as a biomarker for Parkinson's disease.

The diagnosis of Parkinson's disease (PD) occurs after pathogenesis is advanced and many substantia nigra (SN) dopamine neurons have already died. Now that therapies to block this neuronal loss are under development, it is imperative that the disease be diagnosed at earlier stages and that the response to therapies is monitored. Recent studies suggest this can be accomplished by magnetic resonance imaging (MRI) detection of neuromelanin (NM), the characteristic pigment of SN dopaminergic, and locus coeruleus (LC) noradrenergic neurons. NM is an autophagic product synthesized via oxidation of catecholamines and subsequent reactions, and in the SN and LC it increases linearly during normal aging. In PD, however, the pigment is lost when SN and LC neurons die. As shown nearly 25 years ago by Zecca and colleagues, NM's avid binding of iron provides a paramagnetic source to enable electron and nuclear magnetic resonance detection, and thus a means for safe and noninvasive measure in living human brain. Recent technical improvements now provide a means for MRI to differentiate between PD patients and age-matched healthy controls, and should be able to identify changes in SN NM with age in individuals. We discuss how MRI detects NM and how this approach might be improved. We suggest that MRI of NM can be used to confirm PD diagnosis and monitor disease progression. We recommend that for subjects at risk for PD, and perhaps generally for older people, that MRI sequences performed at regular intervals can provide a pre-clinical means to detect presymptomatic PD.

Investigating Brain Connectomic Alterations in Autism Using the Reproducibility of Independent Components Derived from Resting State Functional MRI Data.

Significance: Autism is a developmental disorder that is currently diagnosed using behavioral tests which can be subjective. Consequently, objective non-invasive imaging biomarkers of Autism are being actively researched. The common theme emerging from previous functional magnetic resonance imaging (fMRI) studies is that Autism is characterized by alterations of fMRI-derived functional connections in certain brain networks which may provide a biomarker for objective diagnosis. However, identification of individuals with Autism solely based on these measures has not been reliable, especially when larger sample sizes are taken into consideration. Objective: We surmise that metrics derived from Autism subjects may not be highly reproducible within this group leading to poor generalizability. We hypothesize that functional brain networks that are most reproducible within Autism and healthy Control groups separately, but not when the two groups are merged, may possess the ability to distinguish effectively between the groups. Methods: In this study, we propose a "discover-confirm" scheme based upon the assessment of reproducibility of independent components obtained from resting state fMRI (discover) followed by a clustering analysis of these components to evaluate their ability to discriminate between groups in an unsupervised way (confirm). Results: We obtained cluster purity ranging from 0.695 to 0.971 in a data set of 799 subjects acquired from multiple sites, depending on how reproducible the corresponding components were in each group. Conclusion: The proposed method was able to characterize reproducibility of brain networks in Autism and could potentially be deployed in other mental disorders as well.

Improved Neuroimaging Atlas of the Dentate Nucleus.

The dentate nucleus (DN) of the cerebellum is the major output nucleus of the cerebellum and is rich in iron. Quantitative susceptibility mapping (QSM) provides better iron-sensitive MRI contrast to delineate the boundary of the DN than either T2-weighted images or susceptibility-weighted images. Prior DN atlases used T2-weighted or susceptibility-weighted images to create DN atlases. Here, we employ QSM images to develop an improved dentate nucleus atlas for use in imaging studies. The DN was segmented in QSM images from 38 healthy volunteers. The resulting DN masks were transformed to a common space and averaged to generate the DN atlas. The center of mass of the left and right sides of the QSM-based DN atlas in the Montreal Neurological Institute space was -13.8, -55.8, and -36.4 mm, and 13.8, -55.7, and -36.4 mm, respectively. The maximal probability and mean probability of the DN atlas with the individually segmented DNs in this cohort were 100 and 39.3%, respectively, in contrast to the maximum probability of approximately 75% and the mean probability of 23.4 to 33.7% with earlier DN atlases. Using QSM, which provides superior iron-sensitive MRI contrast for delineating iron-rich structures, an improved atlas for the dentate nucleus has been generated. The atlas can be applied to investigate the role of the DN in both normal cortico-cerebellar physiology and the variety of disease states in which it is implicated.

A New Generation of Brain-Computer Interfaces Driven by Discovery of Latent EEG-fMRI Linkages Using Tensor Decomposition.

A Brain-Computer Interface (BCI) is a setup permitting the control of external devices by decoding brain activity. Electroencephalography (EEG) has been extensively used for decoding brain activity since it is non-invasive, cheap, portable, and has high temporal resolution to allow real-time operation. Due to its poor spatial specificity, BCIs based on EEG can require extensive training and multiple trials to decode brain activity (consequently slowing down the operation of the BCI). On the other hand, BCIs based on functional magnetic resonance imaging (fMRI) are more accurate owing to its superior spatial resolution and sensitivity to underlying neuronal processes which are functionally localized. However, due to its relatively low temporal resolution, high cost, and lack of portability, fMRI is unlikely to be used for routine BCI. We propose a new approach for transferring the capabilities of fMRI to EEG, which includes simultaneous EEG/fMRI sessions for finding a mapping from EEG to fMRI, followed by a BCI run from only EEG data, but driven by fMRI-like features obtained from the mapping identified previously. Our novel data-driven method is likely to discover latent linkages between electrical and hemodynamic signatures of neural activity hitherto unexplored using model-driven methods, and is likely to serve as a template for a novel multi-modal strategy wherein cross-modal EEG-fMRI interactions are exploited for the operation of a unimodal EEG system, leading to a new generation of EEG-based BCIs.

In vivo detection of lateral-ventral tier nigral degeneration in Parkinson's disease.

The objective of this study was to measure neuromelanin-sensitive MRI contrast changes in the lateral-ventral tier of substantia nigra pars compacta in Parkinson's disease (PD). Histopathological studies of PD have demonstrated both massive loss of melanized dopamine neurons and iron accumulation in the substantia nigra pars compacta. Neurodegeneration is most profound in the lateral-ventral tier of this structure. We have previously shown in both healthy controls and individuals with PD that neuromelanin-sensitive MRI and iron-sensitive MRI contrast regions in substantia nigra overlap. This overlap region is located in the lateral-ventral tier. Exploiting this area of contrast overlap for region of interest selection, we developed a semi-automated image processing approach to characterize the lateral-ventral tier in MRI data. Here we apply this approach to measure magnetization transfer contrast, which corresponds to local neuromelanin density, in both the lateral-ventral tier and the entire pars compacta in 22 PD patients and 19 controls. Significant contrast reductions were seen in PD in both the entire pars compacta (P = 0.009) and in its lateral-ventral tier (P = 0.0002); in PD contrast was significantly lower in the lateral-ventral tier than in the entire pars compacta (P = 0.0008). These findings are the first in vivo evidence of the selective vulnerability of this nigral subregion in PD, and this approach may be developed for high impact biomarker applications. Hum Brain Mapp 38:2627-2634, 2017. © 2017 Wiley Periodicals, Inc.

Parkinson's disease-related increase of T2*-weighted hypointensity in substantia nigra pars compacta.

BACKGROUND: In PD, at the time of diagnosis, approximately 50% of melanized dopaminergic neurons in SNpc have died, yet ongoing neuronal death and neuromelanin release with associated neuroinflammation and microglial activation continue, as does local iron accumulation. Previous studies investigating nigral iron accumulation used T2 / T2*-weighted contrasts to define the regions of interest in the SN. Given that T2 / T2*-weighted contrasts lack sensitivity to neuromelanin and thereby SNpc, neuromelanin-sensitive MRI provides better delineation of SNpc and allows the examination of increased iron deposition in SNpc more specifically and accurately. OBJECTIVES: To examine regions of the SNpc, defined by neuromelanin-sensitive MRI, exhibiting iron deposition in PD. METHODS: T1 -weighted and susceptibility weighted imaging data were obtained in a cohort of 82 subjects (54 controls and 28 PD patients). The PD patients were clinically diagnosed with an average UPDRS-III score of 37.9 ± 12.5 in the off medication state. Susceptibility weighted imaging data were analyzed using SNpc regions of interest defined by neuromelanin-sensitive MRI. RESULTS: Compared to control subjects, significantly more hypointense signal was observed in the SNpc defined by neuromelanin-sensitive MRI in the PD patients. In the PD group, the lateral ventral region of SNpc exhibited the greatest increase of hypointensity. This increase in the lateral ventral region of SNpc robustly differentiated PD patients from controls. CONCLUSION: T2*-weighted hypointense signal in the SNpc defined by neuromelanin-sensitive MRI is significantly increased in PD. It is most likely a measure sensitive to PD-related iron deposition and may serve as a robust biomarker of PD. © 2016 International Parkinson and Movement Disorder Society.