Age-related differences in the functional topography of the locus coeruleus and their implications for cognitive and affective functions published on eLife

Average functional gradients of the locus coeruleus in the CamCAN 3T dataset. (Image from the publication.)
Age-related differences in the functional topography of the locus coeruleus and their implications for cognitive and affective functions
Dániel Veréb, Mite Mijalkov, Anna Canal-Garcia, Yu-Wei Chang, Emiliano Gomez-Ruiz, Blanca Zufiria Gerboles, Miia Kivipelto, Per Svenningsson, Henrik Zetterberg, Giovanni Volpe, Matthew Betts, Heidi IL Jacobs, Joana B Pereira
eLife 12, RP87188 (2023)
doi: 10.7554/eLife.87188.3

The locus coeruleus (LC) is an important noradrenergic nucleus that has recently attracted a lot of attention because of its emerging role in cognitive and psychiatric disorders. Although previous histological studies have shown that the LC has heterogeneous connections and cellular features, no studies have yet assessed its functional topography in vivo, how this heterogeneity changes over aging, and whether it is associated with cognition and mood. Here, we employ a gradient-based approach to characterize the functional heterogeneity in the organization of the LC over aging using 3T resting-state fMRI in a population-based cohort aged from 18 to 88 years of age (Cambridge Centre for Ageing and Neuroscience cohort, n=618). We show that the LC exhibits a rostro-caudal functional gradient along its longitudinal axis, which was replicated in an independent dataset (Human Connectome Project [HCP] 7T dataset, n=184). Although the main rostro-caudal direction of this gradient was consistent across age groups, its spatial features varied with increasing age, emotional memory, and emotion regulation. More specifically, a loss of rostral-like connectivity, more clustered functional topography, and greater asymmetry between right and left LC gradients was associated with higher age and worse behavioral performance. Furthermore, participants with higher-than-normal Hospital Anxiety and Depression Scale (HADS) ratings exhibited alterations in the gradient as well, which manifested in greater asymmetry. These results provide an in vivo account of how the functional topography of the LC changes over aging, and imply that spatial features of this organization are relevant markers of LC-related behavioral measures and psychopathology.

Peripheral inflammatory subgroup differences in anterior Default Mode network and multiplex functional network topology are associated with cognition in psychosis published in Brain Behaviour and Immunity

Illustration of resting state network activity. (Image by the Authors of the manuscript.)
Peripheral inflammatory subgroup differences in anterior Default Mode network and multiplex functional network topology are associated with cognition in psychosis
Paulo Lizano, Chelsea Kiely, Mite Mijalkov, Shashwath A. Meda, Sarah K. Keedy, Dung Hoang, Victor Zeng, Olivia Lutz, Joana B. Pereira, Elena I. Ivleva, Giovanni Volpe, Yanxun Xu, Adam M. Lee, Leah H. Rubin, S Kristian Hill, Brett A. Clementz, Carol A. Tamminga, Godfrey D. Pearlson, John A. Sweeney, Elliot S. Gershon, Matcheri S. Keshavan, Jeffrey R. Bishop
Brain Behavior and Immunity, 114, 3-15 (2023)
doi: 10.1016/j.bbi.2023.07.014

Introduction
High-inflammation subgroups of patients with psychosis demonstrate cognitive deficits and neuroanatomical alterations. Systemic inflammation assessed using IL-6 and C-reactive protein may alter functional connectivity within and between resting-state networks, but the cognitive and clinical implications of these alterations remain unknown. We aim to determine the relationships of elevated peripheral inflammation subgroups with resting-state functional networks and cognition in psychosis spectrum disorders.

Methods
Serum and resting-state fMRI were collected from psychosis probands (schizophrenia, schizoaffective, psychotic bipolar disorder) and healthy controls (HC) from the B-SNIP1 (Chicago site) study who were stratified into inflammatory subgroups based on factor and cluster analyses of 13 cytokines (HC Low n = 32, Proband Low n = 65, Proband High n = 29). Nine resting-state networks derived from independent component analysis were used to assess functional and multilayer connectivity. Inter-network connectivity was measured using Fisher z-transformation of correlation coefficients. Network organization was assessed by investigating networks of positive and negative connections separately, as well as investigating multilayer networks using both positive and negative connections. Cognition was assessed using the Brief Assessment of Cognition in Schizophrenia. Linear regressions, Spearman correlations, permutations tests and multiple comparison corrections were used for analyses in R.

Results
Anterior default mode network (DMNa) connectivity was significantly reduced in the Proband High compared to Proband Low (Cohen’s d = -0.74, p = 0.002) and HC Low (d = -0.85, p = 0.0008) groups. Inter-network connectivity between the DMNa and the right-frontoparietal networks was lower in Proband High compared to Proband Low (d = -0.66, p = 0.004) group. Compared to Proband Low, the Proband High group had lower negative (d = 0.54, p = 0.021) and positive network (d = 0.49, p = 0.042) clustering coefficient, and lower multiplex network participation coefficient (d = -0.57, p = 0.014). Network findings in high inflammation subgroups correlate with worse verbal fluency, verbal memory, symbol coding, and overall cognition.

Conclusion
These results expand on our understanding of the potential effects of peripheral inflammatory signatures and/or subgroups on network dysfunction in psychosis and how they relate to worse cognitive performance. Additionally, the novel multiplex approach taken in this study demonstrated how inflammation may disrupt the brain’s ability to maintain healthy co-activation patterns between the resting-state networks while inhibiting certain connections between them.

Functional gradients of the medial parietal cortex in a healthy cohort with family history of sporadic Alzheimer’s disease published in Alzheimer’s Research & Therapy

Spatial maps depicting the strongest connections from the medial parietal cortex to other cortical and subcortical areas in the PREVENT-AD cohort. (Reproduced from the publication.)
Functional gradients of the medial parietal cortex in a healthy cohort with family history of sporadic Alzheimer’s disease
Dániel Veréb, Mite Mijalkov, Yu-Wei Chang, Anna Canal-Garcia, Emiliano Gomez-Ruis, Anne Maass, Sylvia Villeneuve, Giovanni Volpe Joana B. Pereira
Alzheimer’s Research & Therapy 15, 82 (2023)
doi: 10.1186/s13195-023-01228-3

Background
The medial parietal cortex is an early site of pathological protein deposition in Alzheimer’s disease (AD). Previous studies have identified different subregions within this area; however, these subregions are often heterogeneous and disregard individual differences or subtle pathological alterations in the underlying functional architecture. To address this limitation, here we measured the continuous connectivity gradients of the medial parietal cortex and assessed their relationship with cerebrospinal fluid (CSF) biomarkers, ApoE ε4 carriership and memory in asymptomatic individuals at risk to develop AD.

Methods
Two hundred sixty-three cognitively normal participants with a family history of sporadic AD who underwent resting-state and task-based functional MRI using encoding and retrieval tasks were included from the PREVENT-AD cohort. A novel method for characterizing spatially continuous patterns of functional connectivity was applied to estimate functional gradients in the medial parietal cortex during the resting-state and task-based conditions. This resulted in a set of nine parameters that described the appearance of the gradient across different spatial directions. We performed correlation analyses to assess whether these parameters were associated with CSF biomarkers of phosphorylated tau181 (p-tau), total tau (t-tau), and amyloid-ß1-42 (Aß). Then, we compared the spatial parameters between ApoE ε4 carriers and noncarriers, and evaluated the relationship between these parameters and memory.

Results
Alterations involving the superior part of the medial parietal cortex, which was connected to regions of the default mode network, were associated with higher p-tau, t-tau levels as well as lower Aß/p-tau levels during the resting-state condition (p < 0.01). Similar alterations were found in ApoE ε4 carriers compared to non-carriers (p < 0.003). In contrast, lower immediate memory scores were associated with changes in the middle part of the medial parietal cortex, which was connected to inferior temporal and posterior parietal regions, during the encoding task (p = 0.001). No results were found when using conventional connectivity measures.

Conclusions
Functional alterations in the medial parietal gradients are associated with CSF AD biomarkers, ApoE e4 carriership, and lower memory in an asymptomatic cohort with a family history of sporadic AD, suggesting that functional gradients are sensitive to subtle changes associated with early AD stages.

Roadmap on Deep Learning for Microscopy on ArXiv

Spatio-temporal spectrum diagram of microscopy techniques and their applications. (Image by the Authors of the manuscript.)
Roadmap on Deep Learning for Microscopy
Giovanni Volpe, Carolina Wählby, Lei Tian, Michael Hecht, Artur Yakimovich, Kristina Monakhova, Laura Waller, Ivo F. Sbalzarini, Christopher A. Metzler, Mingyang Xie, Kevin Zhang, Isaac C.D. Lenton, Halina Rubinsztein-Dunlop, Daniel Brunner, Bijie Bai, Aydogan Ozcan, Daniel Midtvedt, Hao Wang, Nataša Sladoje, Joakim Lindblad, Jason T. Smith, Marien Ochoa, Margarida Barroso, Xavier Intes, Tong Qiu, Li-Yu Yu, Sixian You, Yongtao Liu, Maxim A. Ziatdinov, Sergei V. Kalinin, Arlo Sheridan, Uri Manor, Elias Nehme, Ofri Goldenberg, Yoav Shechtman, Henrik K. Moberg, Christoph Langhammer, Barbora Špačková, Saga Helgadottir, Benjamin Midtvedt, Aykut Argun, Tobias Thalheim, Frank Cichos, Stefano Bo, Lars Hubatsch, Jesus Pineda, Carlo Manzo, Harshith Bachimanchi, Erik Selander, Antoni Homs-Corbera, Martin Fränzl, Kevin de Haan, Yair Rivenson, Zofia Korczak, Caroline Beck Adiels, Mite Mijalkov, Dániel Veréb, Yu-Wei Chang, Joana B. Pereira, Damian Matuszewski, Gustaf Kylberg, Ida-Maria Sintorn, Juan C. Caicedo, Beth A Cimini, Muyinatu A. Lediju Bell, Bruno M. Saraiva, Guillaume Jacquemet, Ricardo Henriques, Wei Ouyang, Trang Le, Estibaliz Gómez-de-Mariscal, Daniel Sage, Arrate Muñoz-Barrutia, Ebba Josefson Lindqvist, Johanna Bergman
arXiv: 2303.03793

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.

Sex differences in multilayer functional network topology over the course of aging in 37543 UK Biobank participants accepted on Network Neuroscience

Example of the 21 resting-state networks used as nodes and their positive (red) and negative connections (blue) for one of 140 the subjects included in the analyses. (Image by the Authors of the manuscript.)
Sex differences in multilayer functional network topology over the course of aging in 37543 UK Biobank participants
Mite Mijalkov, Dániel Veréb, Oveis Jamialahmadi, Anna Canal-Garcia, Emiliano Gómez-Ruiz, Didac Vidal-Piñeiro, Stefano Romeo, Giovanni Volpe, Joana B. Pereira
Network Neuroscience 1-40 (2022)
doi: 10.1162/netn_a_00286
medRxiv: 10.1101/2022.03.08.22272089

Aging is a major risk factor for cardiovascular and neurodegenerative disorders, with considerable societal and economic implications. Healthy aging is accompanied by changes in functional connectivity between and within resting-state functional networks, which have been associated with cognitive decline. However, there is no consensus on the impact of sex on these age-related functional trajectories. Here, we show that multilayer measures provide crucial information on the interaction between sex and age on network topology, allowing for better assessment of cognitive, structural, and cardiovascular risk factors that have been shown to differ between men and women, as well as providing additional insights into the genetic influences on changes in functional connectivity that occur during aging. In a large cross-sectional sample of 37543 individuals from the UK Biobank cohort, we demonstrate that such multilayer measures that capture the relationship between positive and negative connections are more sensitive to sex-related changes in the whole-brain connectivity patterns and their topological architecture throughout aging, when compared to standard connectivity and topological measures. Our findings indicate that multilayer measures contain previously unknown information on the relationship between sex and age, which opens up new avenues for research into functional brain connectivity in aging.

Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson’s Disease published in Cerebral Cortex

Visual display of the nodes that show significant differences between controls and participants with PD in network measures using the anti-symmetric correlation method. (Image by the Authors.)
Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson’s Disease
Mite Mijalkov, Giovanni Volpe, Joana B Pereira
Cerebral Cortex 32(3), 593–607 (2022)
doi: 10.1093/cercor/bhab237

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by topological abnormalities in large-scale functional brain networks, which are commonly analyzed using undirected correlations in the activation signals between brain regions. This approach assumes simultaneous activation of brain regions, despite previous evidence showing that brain activation entails causality, with signals being typically generated in one region and then propagated to other ones. To address this limitation, here, we developed a new method to assess whole-brain directed functional connectivity in participants with PD and healthy controls using antisymmetric delayed correlations, which capture better this underlying causality. Our results show that whole-brain directed connectivity, computed on functional magnetic resonance imaging data, identifies widespread differences in the functional networks of PD participants compared with controls, in contrast to undirected methods. These differences are characterized by increased global efficiency, clustering, and transitivity combined with lower modularity. Moreover, directed connectivity patterns in the precuneus, thalamus, and cerebellum were associated with motor, executive, and memory deficits in PD participants. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network differences occurring in PD compared with standard methods, opening new opportunities for brain connectivity analysis and development of new markers to track PD progression.

Multiplex Connectome Changes across the Alzheimer’s Disease Spectrum Using Gray Matter and Amyloid Data published in Cerebral Cortex

Brain nodes. (Image taken from the article.)
Multiplex Connectome Changes across the Alzheimer’s Disease Spectrum Using Gray Matter and Amyloid Data
Mite Mijalkov, Giovanni Volpe, Joana B Pereira
Anna Canal-Garcia, Emiliano Gómez-Ruiz, Mite Mijalkov, Yu-Wei Chang, Giovanni Volpe, Joana B Pereira, Alzheimer’s Disease Neuroimaging Initiative
Cerebral Cortex, bhab429 (2022)
doi: 10.1093/cercor/bhab429

The organization of the Alzheimer’s disease (AD) connectome has been studied using graph theory using single neuroimaging modalities such as positron emission tomography (PET) or structural magnetic resonance imaging (MRI). Although these modalities measure distinct pathological processes that occur in different stages in AD, there is evidence that they are not independent from each other. Therefore, to capture their interaction, in this study we integrated amyloid PET and gray matter MRI data into a multiplex connectome and assessed the changes across different AD stages. We included 135 cognitively normal (CN) individuals without amyloid-β pathology (Aβ−) in addition to 67 CN, 179 patients with mild cognitive impairment (MCI) and 132 patients with AD dementia who all had Aβ pathology (Aβ+) from the Alzheimer’s Disease Neuroimaging Initiative. We found widespread changes in the overlapping connectivity strength and the overlapping connections across Aβ-positive groups. Moreover, there was a reorganization of the multiplex communities in MCI Aβ + patients and changes in multiplex brain hubs in both MCI Aβ + and AD Aβ + groups. These findings offer a new insight into the interplay between amyloid-β pathology and brain atrophy over the course of AD that moves beyond traditional graph theory analyses based on single brain networks.

Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson’s Disease published in Cerebral Cortex

Differences between controls and PD participants in nodal network measures. (Image taken from the article.)
Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson’s Disease
Mite Mijalkov, Giovanni Volpe, Joana B Pereira
Cerebral Cortex, bhab237 (2021)
doi: 10.1093/cercor/bhab237

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by topological abnormalities in large-scale functional brain networks, which are commonly analyzed using undirected correlations in the activation signals between brain regions. This approach assumes simultaneous activation of brain regions, despite previous evidence showing that brain activation entails causality, with signals being typically generated in one region and then propagated to other ones. To address this limitation, here, we developed a new method to assess whole-brain directed functional connectivity in participants with PD and healthy controls using antisymmetric delayed correlations, which capture better this underlying causality. Our results show that whole-brain directed connectivity, computed on functional magnetic resonance imaging data, identifies widespread differences in the functional networks of PD participants compared with controls, in contrast to undirected methods. These differences are characterized by increased global efficiency, clustering, and transitivity combined with lower modularity. Moreover, directed connectivity patterns in the precuneus, thalamus, and cerebellum were associated with motor, executive, and memory deficits in PD participants. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network differences occurring in PD compared with standard methods, opening new opportunities for brain connectivity analysis and development of new markers to track PD progression.

Dendritic spines are lost in clusters in patients with Alzheimer’s disease published in Scientific Report

Combined confocal microscopy picture showing a neuron with a soma free of PHF-tau.
Dendritic spines are lost in clusters in patients with Alzheimer’s disease
Mite Mijalkov, Giovanni Volpe, Isabel Fernaud-Espinosa, Javier DeFelipe, Joana B. Pereira, Paula Merino-Serrais
Sci. Rep. 11, 12350 (2021)
doi: 10.1038/s41598-021-91726-x
biorXiv: 10.1101/2020.10.20.346718

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by a deterioration of neuronal connectivity. The pathological accumulation of tau protein in neurons is one of the hallmarks of AD and has been connected to the loss of dendritic spines of pyramidal cells, which are the major targets of cortical excitatory synapses and key elements in memory storage. However, the detailed mechanisms underlying the loss of dendritic spines in patients with AD are still unclear. Here, comparing dendrites with and without tau pathology of AD patients, we show that the presence of tau pathology determines the loss of dendritic spines in blocks, ruling out alternative models where spine loss occurs randomly. Since memory storage has been associated with synaptic clusters, the present results provide a new insight into the mechanisms by which tau drives synaptic damage in AD, paving the way to memory deficits by altering spine organization.

Age-related differences in network structure and dynamic synchrony of cognitive control on biorXiv

Gamma efficiency for older adults.
Age-related differences in network structure and dynamic synchrony of cognitive control
T. Hinault, M. Mijalkov, J.B. Pereira, Giovanni Volpe, A. Bakker, S.M. Courtney
NeuroImage 236, 118070 (2021)
biorXiv: 10.1101/2020.10.09.333567
doi: 10.1016/j.neuroimage.2021.118070

Cognitive trajectories vary greatly across older individuals, and the neural mechanisms underlying these differences remain poorly understood. Here, we propose a mechanistic framework of cognitive variability in older adults, linking the influence of white matter microstructure on fast and effective communications between brain regions. Using diffusion tensor imaging and electroencephalography, we show that individual differences in white matter network organization are associated with network clustering and efficiency in the alpha and high-gamma bands, and that functional network dynamics partly explain individual cognitive control performance in older adults. We show that older individuals with high versus low structural network clustering differ in task-related network dynamics and cognitive performance. These findings were corroborated by investigating magnetoencephalography networks in an independent dataset. This multimodal brain connectivity framework of individual differences provides a holistic account of how differences in white matter microstructure underlie age-related variability in dynamic network organization and cognitive performance.