Dissociable circuits for visual shape learning in the young and aging human brain.

Recognizing objects in cluttered scenes is vital for successful interactions in our complex environments. Learning is known to play a key role in facilitating performance in a wide range of perceptual skills not only in young but also older adults. However, the neural mechanisms that support our ability to improve visual form recognition with training in older age remain largely unknown. Here, we combine behavioral and fMRI measurements to identify the brain circuits involved in the learning of global visual forms in the aging human brain. Our findings demonstrate the learning enhances perceptual sensitivity in the discrimination of visual forms similarly in both young and older adults. However, using fMRI we show that the neural circuits involved in visual form learning differ with age. Our results show that in young adults visual shape learning engages a network of occipitotemporal, parietal, and frontal regions that is known to be involved in perceptual decisions. In contrast, in older adults visual shape learning engages primarily parietal regions, suggesting a stronger role of attentionally-guided learning in older age. Interestingly, learning-dependent changes are maintained in higher occipitotemporal and posterior parietal regions, but not in frontal circuits, when observers perform a control task rather than engaging in a visual form discrimination task. Thus, learning may modulate read-out signals in posterior regions related to global form representations independent of the task, whereas task-dependent frontal activations may reflect changes in sensitivity with training in the context of perceptual decision making

Spatial-temporal modelling of fMRI data through spatially regularized mixture of hidden process models.

Previous work investigated a range of spatio-temporal constraints for fMRI data analysis to provide robust detection of neural activation. We present a mixture-based method for the spatio-temporal modelling of fMRI data. This approach assumes that fMRI time series are generated by a probabilistic superposition of a small set of spatio-temporal prototypes (mixture components). Each prototype comprises a temporal model that explains fMRI signals on a single voxel and the model's "region of influence" through a spatial prior over the voxel space. As the key ingredient of our temporal model, the Hidden Process Model (HPM) framework proposed in Hutchinson et al. (2009) is adopted to infer the overlapping cognitive processes triggered by stimuli. Unlike the original HPM framework, we use a parametric model of Haemodynamic Response Function (HRF) so that biological constraints are naturally incorporated in the HRF estimation. The spatial priors are defined in terms of a parameterised distribution. Thus, the total number of parameters in the model does not depend on the number of voxels. The resulting model provides a conceptually principled and computationally efficient approach to identify spatio-temporal patterns of neural activation from fMRI data, in contrast to most conventional approaches in the literature focusing on the detection of spatial patterns. We first verify the proposed model in a controlled experimental setting using synthetic data. The model is further validated on real fMRI data obtained from a rapid event-related visual recognition experiment (Mayhew et al., 2012). Our model enables us to evaluate in a principled manner the variability of neural activations within individual regions of interest (ROIs). The results strongly suggest that, compared with occipitotemporal regions, the frontal ones are less homogeneous, requiring two HPM prototypes per region. Despite the rapid event-related experimental design, the model is capable of disentangling the perceptual judgement and motor response processes that are both activated in the frontal ROIs. Spatio-temporal heterogeneity in the frontal regions seems to be associated with diverse dynamic localizations of the two hidden processes in different subregions of frontal ROIs

Brain state dynamics differ between eyes open and eyes closed rest

The human brain exhibits spatio-temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) eyes open (EO) and eyes closed (EC) resting-state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG-defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window-based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting-state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha-BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in-depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.</p

The structural and functional connectivity of the posterior cingulate cortex : comparison between deterministic and probabilistic tractography for the investigation of structure–function relationships

The default mode network (DMN) is one of the most studied resting-state networks, and is thought to be involved in the maintenance of consciousness within the alert human brain. Although many studies have examined the functional connectivity (FC) of the DMN, few have investigated its underlying structural connectivity (SC), or the relationship between the two. We investigated this question in fifteen healthy subjects, concentrating on connections to the precuneus/posterior cingulate cortex (PCC), commonly considered as the central node of the DMN. We used group independent component analysis (GICA) and seed-based correlation analysis of fMRI data to quantify FC, and streamline and probabilistic tractography to identify structural tracts from diffusion tensor imaging (DTI) data. We first assessed the presence of structural connections between the DMN regions identified with GICA. Of the 15 subjects, when using the probabilistic approach 15 (15) demonstrated connections between the PCC and mesial prefrontal cortex (mPFC), 11 (15) showed connections from the PCC to the right inferior parietal cortex (rIPC) and 8 (15) to the left IPC. Next, we assessed the strength of FC (magnitude of temporal correlation) and SC (mean fractional anisotropy of reconstructed tracts (streamline), number of super-threshold voxels within the mask region (probabilistic)). The lIPC had significantly reduced FC to the PCC compared to the mPFC and rIPC. No difference in SC strength between connections was found using the streamline approach. For the probabilistic approach, mPFC had significantly lower SC than both IPCs. The two measures of SC strength were significantly correlated, but not for all paired connections. Finally, we observed a significant correlation between SC and FC for both tractography approaches when data were pooled across PCC-lIPL, PCC-rIPL and PCC-mPFC connections, and for some individual paired connections. Our results suggest that the streamline approach is advantageous for characterising the connectivity of long white matter tracts (PCC-mPFC), whilst the probabilistic approach was more reliable at identifying PCC-IPC connections. The direct comparison of FC and SC indicated that pairs of nodes with stronger structural connections also had stronger functional connectivity, and that this was maintained with both tractography approaches. Whilst the definition of SC strength remains controversial, our results could be considered to provide some degree of validation for the measures of SC strength that we have used. Direct comparisons of SC and FC are necessary in order to understand the structural basis of functional connectivity, and to characterise and quantify the changes in the brain's functional architecture that occur as a result of normal physiology or pathology

NeuroImage 84 (2014) 657–671 Contents lists available at ScienceDirect

journal homepage: www.elsevier.com/locate/ynimg Spatial–temporal modelling of fMRI data through spatially regularize

Exploring the advantages of multiband fMRI with simultaneous EEG to investigate coupling between gamma frequency neural activity and the BOLD response in humans

We established an optimal combination of EEG recording during sparse multiband (MB) fMRI that preserves high resolution, whole brain fMRI coverage whilst enabling broad-band EEG recordings which are uncorrupted by MRI gradient artefacts (GAs). We firstly determined the safety of simultaneous EEG recording during MB fMRI. Application of MB factor = 4 produced <1°C peak heating of electrode/hardware during 20-minutes of GE–EPI data acquisition. However, higher SAR sequences require specific safety testing, with greater heating observed using PCASL with MB factor =4. Heating was greatest in the electrocardiogram channel, likely due to it possessing longest lead length. We investigated the effect of MB factoron the temporal signal to noise ratio for a range of GE-EPI sequences (varying MB factor and temporal interval between slice acquisitions). We found that, for our experimental purpose, the optimal acquisition was achieved with MB factor=3, 3mm isotropic voxels and 33 slices providing whole head coverage. This sequence afforded a 2.25s duration quiet period (without GAs) in every 3s TR. Using this sequence we demonstrated the ability to record gamma frequency (55-80Hz) EEG oscillations, in response to right index finger abduction, that are usually obscured by Gas during continuous fMRI data acquisition. In this novel application of EEG - MB fMRI to a motor task we observed a positive correlation between gamma and BOLD responses in bilateral motor regions. These findings support and extend previous work regarding coupling between neural and haemodynamic measures of brain activity in humans and showcase the utility of EEG-MB fMRI for future investigations

Exploring the advantages of multiband fMRI with simultaneous EEG to investigate coupling between gamma frequency neural activity and the BOLD response in humans

We established an optimal combination of EEG recording during sparse multiband (MB) fMRI that preserves high resolution, whole brain fMRI coverage whilst enabling broad-band EEG recordings which are uncorrupted by MRI gradient artefacts (GAs). We firstly determined the safety of simultaneous EEG recording during MB fMRI. Application of MB factor = 4 produced <1°C peak heating of electrode/hardware during 20-minutes of GE–EPI data acquisition. However, higher SAR sequences require specific safety testing, with greater heating observed using PCASL with MB factor =4. Heating was greatest in the electrocardiogram channel, likely due to it possessing longest lead length. We investigated the effect of MB factoron the temporal signal to noise ratio for a range of GE-EPI sequences (varying MB factor and temporal interval between slice acquisitions). We found that, for our experimental purpose, the optimal acquisition was achieved with MB factor=3, 3mm isotropic voxels and 33 slices providing whole head coverage. This sequence afforded a 2.25s duration quiet period (without GAs) in every 3s TR. Using this sequence we demonstrated the ability to record gamma frequency (55-80Hz) EEG oscillations, in response to right index finger abduction, that are usually obscured by Gas during continuous fMRI data acquisition. In this novel application of EEG - MB fMRI to a motor task we observed a positive correlation between gamma and BOLD responses in bilateral motor regions. These findings support and extend previous work regarding coupling between neural and haemodynamic measures of brain activity in humans and showcase the utility of EEG-MB fMRI for future investigations

Gender Specific Re-organization of Resting-State Networks in Older Age

Advancing age is commonly associated with changes in both brain structure and function. Recently, the suggestion that alterations in brain connectivity may drive disruption in cognitive abilities with age has been investigated. However, the interaction between the effects of age and gender on the reorganisation of resting-state networks is not fully understood. This study sought to investigate the effect of both age and gender on intra- and inter-network functional connectivity (FC) and the extent to which RSN node definition may alter with older age. We obtained resting-state functional magnetic resonance images from younger (n=20) and older (n=20) adults and assessed the FC of three main cortical networks: default mode (DMN), dorsal attention (DAN) and saliency (SN). Older adults exhibited reduced DMN intra-network FC and increased inter-network FC between the anterior cingulate cortex (ACC) and nodes of the DAN, in comparison to younger participants. Furthermore, this increase in ACC-DAN inter-network FC with age was driven largely by male participants. However, further analyses suggested that the spatial location of ACC, bilateral anterior insula and orbitofrontal cortex RSN nodes changed with older age and that age-related gender differences in FC may reflect spatial re-organisation rather than increases or decreases in FC strength alone. These differences in both the FC and spatial distribution of RSNs between younger and older adults provide evidence of reorganisation of fundamental brain networks with age, which is modulated by gender. These results highlight the need to further investigate changes in both intra- and inter- network FC with age, whilst also exploring the modifying effect of gender. They also emphasise the difficulties in directly comparing the FC of RSN nodes between groups and suggest that caution should be taken when using the same RSN node definitions for different age or patient groups to investigate FC