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Peter Fransson

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14 papers

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YNIMG Journal 2023 Journal Article

Brain network integration, segregation and quasi‐periodic activation and deactivation during tasks and rest

Peter Fransson
Marika Strindberg

Previous studies have shown that a re-organization of the brain's functional connectome expressed in terms of network integration and segregation may play a pivotal role for brain function. However, it has been proven difficult to fully capture both processes independently in a single methodological framework. In this study, by starting from pair-wise assessments of instantaneous phase synchronization and community membership, we assemble spatiotemporally flexible networks that reflect changes in integration/segregation that occur at a spectrum of spatial as well as temporal scales. This is achieved by iteratively assembling smaller networks into larger units under the constraint that the smaller units should be internally integrated, i.e. belong to the same community. The assembled subnetworks can be partly overlapping and differ in size across time. Our results show that subnetwork integration and segregation occur simultaneously in the brain. During task performance, global changes in synchronization between networks arise that are tied to the underlying temporal design of the experiment. We show that a hallmark property of the dynamics of the brain's functional connectome is a presence of quasi-periodic patterns of network activation and deactivation, which during task performance becomes intertwined with the underlying temporal structure of the experimental paradigm. Additionally, we show that the degree of network integration throughout a n-back working memory task is correlated to performance.

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YNIMG Journal 2021 Journal Article

Spatiotemporally flexible subnetworks reveal the quasi-cyclic nature of integration and segregation in the human brain

Marika Strindberg
Peter Fransson
Joana Cabral
Ulrika Ådén

Though the organization of functional brain networks is modular at its core, modularity does not capture the full range of dynamic interactions between individual brain areas nor at the level of subnetworks. In this paper we present a hierarchical model that represents both flexible and modular aspects of intrinsic brain organization across time by constructing spatiotemporally flexible subnetworks. We also demonstrate that segregation and integration are complementary and simultaneous events. The method is based on combining the instantaneous phase synchrony analysis (IPSA) framework with community detection to identify a small, yet representative set of subnetwork components at the finest level of spatial granularity. At the next level, subnetwork components are combined into spatiotemporally flexibly subnetworks where temporal lag in the recruitment of areas within subnetworks is captured. Since individual brain areas are permitted to be part of multiple interleaved subnetworks, both modularity as well as more flexible tendencies of connectivity are accommodated for in the model. Importantly, we show that assignment of subnetworks to the same community (integration) corresponds to positive phase coherence within and between subnetworks, while assignment to different communities (segregation) corresponds to negative phase coherence or orthogonality. Together with disintegration, i.e. the breakdown of internal coupling within subnetwork components, orthogonality facilitates reorganization between subnetworks. In addition, we show that the duration of periods of integration is a function of the coupling strength within subnetworks and subnetwork components which indicates an underlying metastable dynamical regime. Based on the main tendencies for either integration or segregation, subnetworks are further clustered into larger meta-networks that are shown to correspond to combinations of core resting-state networks. We also demonstrate that subnetworks and meta-networks are coarse graining strategies that captures the quasi-cyclic recurrence of global patterns of integration and segregation in the brain. Finally, the method allows us to estimate in broad terms the spectrum of flexible and/or modular tendencies for individual brain areas.

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YNIMG Journal 2021 Journal Article

Whole-brain modelling of resting state fMRI differentiates ADHD subtypes and facilitates stratified neuro-stimulation therapy

Behzad Iravani
Artin Arshamian
Peter Fransson
Neda Kaboodvand

Recent advances in non-linear computational and dynamical modelling have opened up the possibility to parametrize dynamic neural mechanisms that drive complex behavior. Importantly, building models of neuronal processes is of key importance to fully understand disorders of the brain as it may provide a quantitative platform that is capable of binding multiple neurophysiological processes to phenotype profiles. In this study, we apply a newly developed adaptive frequency-based model of whole-brain oscillations to resting-state fMRI data acquired from healthy controls and a cohort of attention deficit hyperactivity disorder (ADHD) subjects. As expected, we found that healthy control subjects differed from ADHD in terms of attractor dynamics. However, we also found a marked dichotomy in neural dynamics within the ADHD cohort. Next, we classified the ADHD group according to the level of distance of each individual's empirical network from the two model-based simulated networks. Critically, the model was mirrored in the empirical behavior data with the two ADHD subgroups displaying distinct behavioral phenotypes related to emotional instability (i.e., depression and hypomanic personality traits). Finally, we investigated the applicability and feasibility of our whole-brain model in a therapeutic setting by conducting in silico excitatory stimulations to parsimoniously mimic clinical neuro-stimulation paradigms in ADHD. We tested the effect of stimulating any individual brain region on the key network measures derived from the simulated brain network and its contribution in rectifying the brain dynamics to that of the healthy brain, separately for each ADHD subgroup. This showed that this was indeed possible for both subgroups. However, the current effect sizes were small suggesting that the stimulation protocol needs to be tailored at the individual level. These findings demonstrate the potential of this new modelling framework to unveil hidden neurophysiological profiles and establish tailored clinical interventions.

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YNIMG Journal 2020 Journal Article

Dynamic synergetic configurations of resting-state networks in ADHD

Neda Kaboodvand
Behzad Iravani
Peter Fransson

Attention deficit hyperactivity disorder (ADHD) is characterized by high distractibility and impaired executive functions. Notably, there is mounting evidence suggesting that ADHD could be regarded as a default mode network (DMN) disorder. In particular, failure in regulating the dynamics of activity and interactions of the DMN and cognitive control networks have been hypothesized as the main source of task interference causing attentional problems. On the other hand, previous studies indicated pronounced fluctuations in the strength of functional connections over time, particularly for the inter-network connections between the DMN and fronto-parietal control networks. Hence, characterization of connectivity disturbances in ADHD requires a thorough assessment of time-varying functional connectivity (FC). In this study, we proposed a dynamical systems perspective to assess how the DMN over time recruits different configurations of network segregation and integration. Specifically, we were interested in configurations for which both intra- and inter-network connections are retained, as opposed to commonly used methods which assess network segregation as a single measure. From resting-state fMRI data, we extracted three different stable configurations of FC patterns for the DMN, namely synergies. We provided evidence supporting our hypothesis that ADHD differs compared to controls, both in terms of recruitment rate and topology of specific synergies between resting-state networks. In addition, we found a relationship between synergetic cooperation patterns of the DMN with cognitive control networks and a behavioral measure which is sensitive to ADHD-related symptoms, namely the Stroop color-word task.

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YNIMG Journal 2020 Journal Article

Temporal flow of hubs and connectivity in the human brain

Peter Fransson
William H. Thompson

Hubs in brain network connectivity have previously been observed using neuroimaging techniques and are generally believed to be of pivotal importance to establish and maintain a functional platform on which cognitively meaningful and energy-efficient neuronal communication can occur. However, little is known if hubs are static (i. e. a brain region is always a hub) or if these properties change over time (i. e. brain regions fluctuate in their ‘hubness’). To address this question, we introduce two new methodological concepts, the flow of brain connectivity and node penalized shortest paths which are then applied to time-varying functional connectivity fMRI BOLD data. We show that the constellations of active hubs change over time in a non-trivial way and that activity of hubs is dependent on the temporal scale of investigation. Slower fluctuations in the number of active hubs that exceeded the degree expected by chance alone were detected primarily in subcortical structures. Moreover, we observed faster fluctuations in hub activity residing predominately in the default mode network that suggests dynamic events in brain connectivity. Our results suggest that the temporal behavior of connectivity hubs is a multilayered and complex issue where method-specific properties of temporal sensitivity to time-varying connectivity must be taken into account. We discuss our results in relation to the on-going discussion of the existence of discrete and stable states in the resting-brain and the role of network hubs in providing a scaffold for neuronal communication across time.

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YNIMG Journal 2018 Journal Article

A common framework for the problem of deriving estimates of dynamic functional brain connectivity

William Hedley Thompson
Peter Fransson

The research field of dynamic functional connectivity explores the temporal properties of brain connectivity. To date, many methods have been proposed, which are based on quite different assumptions. In order to understand in which way the results from different techniques can be compared to each other, it is useful to be able to formulate them within a common theoretical framework. In this study, we describe such a framework that is suitable for many of the dynamic functional connectivity methods that have been proposed. Our overall intention was to derive a theoretical framework that was constructed such that a wide variety of dynamic functional connectivity techniques could be expressed and evaluated within the same framework. At the same time, care was given to the fact that key features of each technique could be easily illustrated within the framework and thus highlighting critical assumptions that are made. We aimed to create a common framework which should serve to assist comparisons between different analytical methods for dynamic functional brain connectivity and promote an understanding of their methodological advantages as well as potential drawbacks.

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YNIMG Journal 2018 Journal Article

Brain network segregation and integration during an epoch-related working memory fMRI experiment

Peter Fransson
Björn C. Schiffler
William Hedley Thompson

The characterization of brain subnetwork segregation and integration has previously focused on changes that are detectable at the level of entire sessions or epochs of imaging data. In this study, we applied time-varying functional connectivity analysis together with temporal network theory to calculate point-by-point estimates in subnetwork segregation and integration during an epoch-based (2-back, 0-back, baseline) working memory fMRI experiment as well as during resting-state. This approach allowed us to follow task-related changes in subnetwork segregation and integration at a high temporal resolution. At a global level, the cognitively more taxing 2-back epochs elicited an overall stronger response of integration between subnetworks compared to the 0-back epochs. Moreover, the visual, sensorimotor and fronto-parietal subnetworks displayed characteristic and distinct temporal profiles of segregation and integration during the 0- and 2-back epochs. During the interspersed epochs of baseline, several subnetworks, including the visual, fronto-parietal, cingulo-opercular and dorsal attention subnetworks showed pronounced increases in segregation. Using a drift diffusion model we show that the response time for the 2-back trials are correlated with integration for the fronto-parietal subnetwork and correlated with segregation for the visual subnetwork. Our results elucidate the fast-evolving events with regard to subnetwork integration and segregation that occur in an epoch-related task fMRI experiment. Our findings suggest that minute changes in subnetwork integration are of importance for task performance.

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YNICL Journal 2016 Journal Article

Functional resting-state fMRI connectivity correlates with serum levels of the S100B protein in the acute phase of traumatic brain injury

William Hedley Thompson
Eric Peter Thelin
Anders Lilja
Bo-Michael Bellander
Peter Fransson

The S100B protein is an intra-cellular calcium-binding protein that mainly resides in astrocytes in the central nervous system. The serum level of S100B is used as biomarker for the severity of brain damage in traumatic brain injury (TBI) patients. In this study we investigated the relationship between intrinsic resting-state brain connectivity, measured 1-22 days (mean 8 days) after trauma, and serum levels of S100B in a patient cohort with mild-to-severe TBI in need of neuro-intensive care in the acute phase. In line with previous investigations, our results show that the peak level of S100B acquired during the acute phase of TBI was negatively correlated with behavioral measures (Glasgow Outcome Score, GOS) of functional outcome assessed 6 to 12 months post injury. Using a multi-variate pattern analysis-informed seed-based correlation analysis, we show that the strength of resting-state brain connectivity in multiple resting-state networks was negatively correlated with the peak of serum levels of S100B. A negative correspondence between S100B peak levels recorded 12-36 h after trauma and intrinsic connectivity was found for brain regions located in the default mode, fronto-parietal, visual and motor resting-state networks. Our results suggest that resting-state brain connectivity measures acquired during the acute phase of TBI is concordant with results obtained from molecular biomarkers and that it may hold a capacity to predict long-term cognitive outcome in TBI patients.

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YNICL Journal 2015 Journal Article

Presurgical language lateralization assessment by fMRI and dichotic listening of pediatric patients with intractable epilepsy

Fritjof Norrelgen
Anders Lilja
Martin Ingvar
Per Åmark
Peter Fransson

OBJECTIVE: The aim of this study was to evaluate the clinical use of a method to assess hemispheric language dominance in pediatric candidates for epilepsy surgery. The method is designed for patients but has previously been evaluated with healthy children. METHODS: Nineteen patients, 8-18 years old, with intractable epilepsy and candidates for epilepsy surgery were assessed. The assessment consisted of two functional MRI protocols (fMRI) intended to target frontal and posterior language networks respectively, and a behavioral dichotic listening task (DL). Regional left/right indices for each fMRI task from the frontal, temporal and parietal lobe were calculated, and left/right indices of the DL task were calculated from responses of consonants and vowels, separately. A quantitative analysis of each patient's data set was done in two steps based on clearly specified criteria. First, fMRI data and DL data were analyzed separately to determine whether the result from each of these assessments were conclusive or not. Thereafter, the results from the individual assessments were combined to reach a final conclusion regarding hemispheric language dominance. RESULTS: For 14 of the 19 subjects (74%) a conclusion was reached about their hemispheric language dominance. Nine subjects had a left-sided and five subjects had a right-sided hemispheric dominance. In three cases (16%) DL provided critical data to reach a conclusive result. CONCLUSIONS: The success rate of conclusive language lateralization assessments in this study is comparable to reported rates on similar challenged pediatric populations. The results are promising but data from more patients than in the present study will be required to conclude on the clinical applicability of the method.

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YNIMG Journal 2015 Journal Article

The frequency dimension of fMRI dynamic connectivity: Network connectivity, functional hubs and integration in the resting brain

William Hedley Thompson
Peter Fransson

The large-scale functional MRI connectome of the human brain is composed of multiple resting-state networks (RSNs). However, the network dynamics, such as integration and segregation between and within RSNs is largely unknown. To address this question we created high-resolution “frequency graphlets”, connectivity matrices derived across the low-frequency spectrum of the BOLD fMRI resting-state signal (0. 01–0. 1Hz) in a cohort of 100 subjects. We then apply and compare graph theoretical measures across the frequency graphlets. Our results show that the within- and between-network connectivity and presence of functional hubs shift as a function of frequency. Furthermore, we show that the small world network property peaks at different frequencies with corresponding spatial connectivity profiles. We conclude that the frequency dependence of the network connectivity and the spatial configuration of functional hubs suggest that the dynamics of large-scale network integration and segregation operate at different time scales.

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YNIMG Journal 2008 Journal Article

The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis

Peter Fransson
Guillaume Marrelec

Recent research has shown that intrinsic brain activity as observed by functional magnetic resonance imaging (fMRI) manifest itself as coherent signal changes in networks encompassing brain regions that span long-range neuronal pathways. One of these networks, the so called default mode network, has become the primary target in recent investigations to link intrinsic activity to cognition and how intrinsic signal changes may be altered in disease. In this study we assessed functional connectivity within the default mode network during both rest and a continuous working memory task on a region-by-region basis using partial correlation analysis, a data-driven method that provides insight into effective connectivity within neuronal networks. Prominent features of functional connectivity within the default mode network included an overall strong level of interaction between the precuneus/posterior cingulate region and the rest of the default mode network, as well as a high degree of interaction between the left and right medial temporal lobes combined with weak interactions between the medial temporal lobes and the rest of the default mode network. Additionally, we found support for strong interactions between the precuneus/posterior cingulate cortex and the left inferior parietal lobe as well as between the dorsal and ventral sections of the medial prefrontal cortex. The suggested pivotal role of the precuneus/posterior cingulate cortex in the default mode network is discussed.

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YNIMG Journal 2006 Journal Article

fMRI activity in the medial temporal lobe during famous face processing

Christina Elfgren
Danielle van Westen
Ulla Passant
Elna-Marie Larsson
Peter Mannfolk
Peter Fransson

The current event-related fMRI study examined the relative involvement of different parts of the medial temporal lobe (MTL), particularly the contribution of hippocampus and perirhinal cortex, in either intentional or incidental recognition of famous faces in contrast to unfamiliar faces. Our intention was to further explore the controversial contribution of MTL in the processing of semantic memory tasks. Subjects viewed a sequence of famous and unfamiliar faces. Two tasks were used encouraging attention to either fame or gender. In the fame task, the subjects were requested to identify the person when seeing his/her face and also to try to generate the name of this person. In the gender task, the subjects were asked to conduct a judgement of a person's gender when seeing his/her face. The visual processing was hence directed to gender and thereby expected to diminish attention to semantic information leading only to a “passive” registration of famous and non-familiar faces. Recognition of famous faces, in both contrasts, produced significant activations in the MTL. First, during the intentional recognition (the person identification task) increased activity was observed in the anterolateral part of left hippocampus, in proximity to amygdala. Second, during the incidental recognition of famous faces (the gender classification task), there was increased activity in the left posterior MTL with focus in the perirhinal cortex. Our results suggest that the hippocampus may be centrally involved in the intentional retrieval of semantic memories while the perirhinal cortex is associated with the incidental recognition of semantic information.

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YNIMG Journal 2001 Journal Article

Functional MRI of the Human Amygdala?

Klaus-Dietmar Merboldt
Peter Fransson
Harald Bruhn
Jens Frahm

Inview of an increasing number of publications that deal with functional mapping of the human amygdala using blood oxygenation-level-dependent (BOLD) magnetic resonance imaging, we reevaluated the underlying image quality of T2*-weighted echoplanar imaging (EPI) and fast low angle shot (FLASH) sequences at 2. 0-T with regard to susceptibility-induced signal losses and geometric distortions. Apart from the timing of the gradient echoes, the degree of susceptibility influences is controlled by the image voxel size. Whereas published amygdala studies report voxel sizes ranging from 22 to 125 μl, the present results suggest that reliable imaging of the amygdala with BOLD sensitivity requires voxel sizes of 4 to 8 μl or less. Preferentially, acquisitions should be performed with a coronal section orientation. Although high-resolution BOLD MRI is at the expense of temporal resolution and volume coverage, it seems to provide the only solution to this physical problem.

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YNIMG Journal 1999 Journal Article

MRI of Functional Deactivation: Temporal and Spatial Characteristics of Oxygenation-Sensitive Responses in Human Visual Cortex

Peter Fransson
Gunnar Krüger
Klaus-Dietmar Merboldt
Jens Frahm

Magnetic resonance imaging (MRI) of neuronal “activation” relies on the elevation of blood flow and oxygenation and a related increase of the blood oxygenation level-dependent (BOLD) MRI signal. Because most cognitive paradigms involve both switches from a low degree of activity to a high degree of activity and vice versa, we have undertaken a baseline study of the temporal and spatial characteristics of positive and negative BOLD MRI responses in human visual cortex. Experiments were performed at 2. 0 T using a multislice gradient-echo EPI sequence (TR=1 s, mean TE=54 ms, flip angle 50°) at 2 × 2-mm2spatial resolution. Activation and “deactivation” processes were accomplished by reversing the order of stimulus presentations in paradigms using homogeneous gray light and an alternating checkerboard as distinct functional states. For sustained stimulation (≥60 s) the two conditions resulted in markedly different steady-state BOLD MRI signal strengths. The transient responses to brief stimulation (≤18 s) differed insofar as activation processes temporally separate positive BOLD and negative undershoot effects by about 10 s, whereas negative BOLD effects and undershoot contributions overlap for deactivation processes. Apart from differences in stimulus features (e. g. , motion) the used activation and deactivation protocols revealed similar maps of neuronal activity changes.

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