NeuroImage

Validating the sensitivity of inhomogeneous magnetization transfer (ihMT) MRI to myelin with fluorescence microscopy Publication date: 1 October 2019 Source: NeuroImage, Volume 199 Author(s): G. Duhamel, V.H. Prevost, M. Cayre, A. Hertanu, S. Mchinda, V.N. Carvalho, G. Varma, P. Durbec, D.C. Alsop, O.M. Girard Abstract Inhomogeneous Magnetization Transfer (ihMT) is a development from the MT MRI technique. IhMT can be considered as a dipolar order relaxation time (T1D) weighted imaging modality whose signal has shown an enhanced selectivity for myelin-rich structures. However, a formal validation of the ihMT sensitivity relative to a gold standard myelin density measurement has not yet been reported. To address this need, we compared ihMT MRI with green fluorescence protein (GFP) microscopy, in a study performed on genetically-modified plp-GFP mice, considered as a reference technique for myelin-content assessment. Various ihMT protocols consisting of variable T1D-filtering and radiofrequency power temporal distributions, were used for comparison with fluorescence microscopy. Strong and significant linear relationships (r2 (0.87–0.96), p < 0.0001) were found between GFP and ihMT ratio signals across brain regions for all tested protocol variants. Conventional MT ratios showed weaker correlations (r2 (0.24–0.78), p ≤ 0.02) and a much larger signal fraction unrelated to myelin, hence corresponding to a much lower specificity for myelin. T1D-filtering reduced the ihMT signal fraction not attributed to myelin by almost twofold relative to zero filtering suggesting that at least half of the unrelated signal has a substantially shorter T1D than myelin. Overall, these results strongly support the sensitivity of ihMT to myelin content. Graphical abstract Validation of ihMT as a myelin sensitive technique by comparison of ihMTR values derived from various RF irradiation preparations with GFP fluorescence intensity, measured in different brain structures.

Different patterns of cortical activity in females and males during spatial long-term memory Publication date: 1 October 2019 Source: NeuroImage, Volume 199 Author(s): Dylan S. Spets, Brittany M. Jeye, Scott D. Slotnick Abstract It is generally assumed that identical neural regions mediate the same cognitive functions in females and males. However, anatomic and molecular sex differences exist in the brain, including in regions associated with long-term memory, which suggests there may be functional differences. The present functional magnetic resonance imaging (fMRI) investigation aimed to identify the differences and similarities in brain activity between females and males during spatial long-term memory. During encoding, abstract shapes were presented to the left or right of fixation. During retrieval, shapes were presented at fixation and participants made "old-left" or "old-right" judgments. For both females and males, spatial memory hits versus misses produced activity in regions commonly associated with visual long-term memory; however, the activations were almost completely distinct between the sexes. An interaction analysis revealed sex-specific activity for males in visual processing regions, the left putamen, the right caudate nucleus, and bilateral cerebellum, and sex-specific activity for females in the parietal cortex. A targeted anatomic region-of-interest (ROI) analysis identified sex-specific activity for males and females in the left hippocampus and language processing cortex, respectively. A multi-voxel pattern correlation analysis within functional ROIs between all pairs of participants showed greater within-sex than between-sex correlations, indicating the differential activations were due to sex differences rather than other individual differences between groups. These results indicate that spatial long-term memory is mediated by largely different brain regions in females and males. These findings have major implications for the field of cognitive neuroscience, where it is common practice to collapse across sex.

Dissociating refreshing and elaboration and their impacts on memory Publication date: 1 October 2019 Source: NeuroImage, Volume 199 Author(s): Lea M. Bartsch, Vanessa M. Loaiza, Lutz Jäncke, Klaus Oberauer, Jarrod A. Lewis-Peacock Abstract Maintenance of information in working memory (WM) is assumed to rely on refreshing and elaboration, but clear mechanistic descriptions of these cognitive processes are lacking, and it is unclear whether they are simply two labels for the same process. This fMRI study investigated the extent to which refreshing, elaboration, and repeating of items in WM are distinct neural processes with dissociable behavioral outcomes in WM and long-term memory (LTM). Multivariate pattern analyses of fMRI data revealed differentiable neural signatures for these processes, which we also replicated in an independent sample of older adults. In some cases, the degree of neural separation within an individual predicted their memory performance. Elaboration improved LTM, but not WM, and this benefit increased as its neural signature became more distinct from repetition. Refreshing had no impact on LTM, but did improve WM, although the neural discrimination of this process was not predictive of the degree of improvement. These results demonstrate that refreshing and elaboration are separate processes that differently contribute to memory performance.

In vivo widefield calcium imaging of the mouse cortex for analysis of network connectivity in health and brain disease Publication date: 1 October 2019 Source: NeuroImage, Volume 199 Author(s): Julia V. Cramer, Benno Gesierich, Stefan Roth, Martin Dichgans, Marco Düring, Arthur Liesz Abstract The organization of brain areas in functionally connected networks, their dynamic changes, and perturbations in disease states are subject of extensive investigations. Research on functional networks in humans predominantly uses functional magnetic resonance imaging (fMRI). However, adopting fMRI and other functional imaging methods to mice, the most widely used model to study brain physiology and disease, poses major technical challenges and faces important limitations. Hence, there is great demand for alternative imaging modalities for network characterization. Here, we present a refined protocol for in vivo widefield calcium imaging of both cerebral hemispheres in mice expressing a calcium sensor in excitatory neurons. We implemented a stringent protocol for minimizing anesthesia and excluding movement artifacts which both imposed problems in previous approaches. We further adopted a method for unbiased identification of functional cortical areas using independent component analysis (ICA) on resting-state imaging data. Biological relevance of identified components was confirmed using stimulus-dependent cortical activation. To explore this novel approach in a model of focal brain injury, we induced photothrombotic lesions of the motor cortex, determined changes in inter- and intrahemispheric connectivity at multiple time points up to 56 days post-stroke and correlated them with behavioral deficits. We observed a severe loss in interhemispheric connectivity after stroke, which was partially restored in the chronic phase and associated with corresponding behavioral motor deficits. Taken together, we present an improved widefield calcium imaging tool accounting for anesthesia and movement artifacts, adopting an advanced analysis pipeline based on human fMRI algorithms and with superior sensitivity to recovery mechanisms in mouse models compared to behavioral tests. This tool will enable new studies on interhemispheric connectivity in murine models with comparability to human imaging studies for a wide spectrum of neuroscience applications in health and disease.

PSACNN: Pulse sequence adaptive fast whole brain segmentation Publication date: 1 October 2019 Source: NeuroImage, Volume 199 Author(s): Amod Jog, Andrew Hoopes, Douglas N. Greve, Koen Van Leemput, Bruce Fischl Abstract With the advent of convolutional neural networks (CNN), supervised learning methods are increasingly being used for whole brain segmentation. However, a large, manually annotated training dataset of labeled brain images required to train such supervised methods is frequently difficult to obtain or create. In addition, existing training datasets are generally acquired with a homogeneous magnetic resonance imaging (MRI) acquisition protocol. CNNs trained on such datasets are unable to generalize on test data with different acquisition protocols. Modern neuroimaging studies and clinical trials are necessarily multi-center initiatives with a wide variety of acquisition protocols. Despite stringent protocol harmonization practices, it is very difficult to standardize the gamut of MRI imaging parameters across scanners, field strengths, receive coils etc., that affect image contrast. In this paper we propose a CNN-based segmentation algorithm that, in addition to being highly accurate and fast, is also resilient to variation in the input acquisition. Our approach relies on building approximate forward models of pulse sequences that produce a typical test image. For a given pulse sequence, we use its forward model to generate plausible, synthetic training examples that appear as if they were acquired in a scanner with that pulse sequence. Sampling over a wide variety of pulse sequences results in a wide variety of augmented training examples that help build an image contrast invariant model. Our method trains a single CNN that can segment input MRI images with acquisition parameters as disparate as T1-weighted and T2-weighted contrasts with only T1-weighted training data. The segmentations generated are highly accurate with state-of-the-art results (overall Dice overlap=0.94), with a fast run time (≈ 45 s), and consistent across a wide range of acquisition protocols.

The influence of brain iron on myelin water imaging Publication date: 1 October 2019 Source: NeuroImage, Volume 199 Author(s): Christoph Birkl, Anna Maria Birkl-Toeglhofer, Verena Endmayr, Romana Höftberger, Gregor Kasprian, Claudia Krebs, Johannes Haybaeck, Alexander Rauscher Abstract With myelin playing a vital role in normal brain integrity and function and thus in various neurological disorders, myelin sensitive magnetic resonance imaging (MRI) techniques are of great importance. In particular, multi-exponential T2 relaxation was shown to be highly sensitive to myelin. The myelin water imaging (MWI) technique allows to separate the T2 decay into short components, specific to myelin water, and long components reflecting the intra- and extracellular water. The myelin water fraction (MWF) is the ratio of the short components to all components. In the brain's white matter (WM), myelin and iron are closely linked via the presence of iron in the myelin generating oligodendrocytes. Iron is known to decrease T2 relaxation times and may therefore mimic myelin. In this study, we investigated if variations in WM iron content can lead to apparent MWF changes. We performed MWI in post mortem human brain tissue prior and after chemical iron extraction. Histology for iron and myelin confirmed a decrease in iron content and no change in myelin content after iron extraction. In MRI, iron extraction lead to a decrease in MWF by 26%–28% in WM. Thus, a change in MWF does not necessarily reflect a change in myelin content. This observation has important implications for the interpretation of MWI findings in previously published studies and future research.

Retrieval orientation alters neural activity during autobiographical memory recollection Publication date: 1 October 2019 Source: NeuroImage, Volume 199 Author(s): Lauri Gurguryan, Signy Sheldon Abstract When an autobiographical memory is retrieved, the underlying memory representation is constructed by flexibly activating a broad neural network. As such, the content used to reconstruct a memory can bias activity within this neural network. Here, we tested the hypothesis that focusing on the conceptual and contextual aspects of a memory to construct a memory representation will recruit distinct neural subsystems. To test this hypothesis, we measured neural activity as participants retrieved memories under retrieval orientations that biased remembering towards these elements of a past autobiographical experience. In an MRI scanner, participants first retrieved autobiographical memories and then were re-oriented towards the conceptual or contextual elements of that memory. They then used this re-oriented content (conceptual or contextual elements) to access and elaborate upon a new autobiographical memory. Confirming our hypothesis, we found a neural dissociation between these retrieval orientation conditions that aligned with established models of memory. We also found evidence that this neural dissociation was most prominent when the re-oriented mnemonic content was used to access a new memory. Altogether, the reported results provide critical insight into how and when retrieval orientations alter neural support for autobiographical memory retrieval and inform on the neural organization of autobiographical knowledge.

Neural correlates of anticipatory cardiac deceleration and its association with the speed of perceptual decision-making, in young and older adults Publication date: 1 October 2019 Source: NeuroImage, Volume 199 Author(s): Maria J. Ribeiro, Miguel Castelo-Branco Abstract Warning stimuli in sensorimotor tasks induce a state of preparedness characterized by increased alertness, focused attention and immobility. This state of attentive anticipation is associated with heart rate deceleration. Ageing affects the amplitude of the anticipatory cardiac deceleration responses; yet, the impact of this physiological change on cognitive performance is still to be elucidated. In fact, how cardiac deceleration relates to brain function and cognitive performance in the context of perceptual decision-making and different levels of decision complexity remains unknown. Here, we aimed to investigate the relationship between cardiac deceleration, brain function, and performance in perceptual decision tasks and how these associate with age-related changes. We measured simultaneously the electrocardiogram, the pupilogram, and the electroencephalogram in 36 young and 39 older adults, while they were engaged in two auditory cued reaction time tasks: a detection task and a go/no-go task requiring inhibitory control. We observed robust cardiac deceleration responses that increased with increasing task complexity. Notably, stronger modulation of the cardiac response across tasks was associated with the ability to maintain response speed as decision complexity increased suggesting a link between cardiac deceleration and facilitation of perceptual decisions. Additionally, cardiac deceleration appears to have a cortical origin as it correlated with frontocentral event-related potentials. In contrast, beta oscillations at baseline and task-related beta suppression were not predictive of cardiac deceleration suggesting a dissociation between sensorimotor oscillatory activity and this cardiac response. Importantly, we found age-related changes in anticipatory cardiac deceleration associated with deficits in perceptual decision-making.

Pre-stimulus brain state predicts auditory pattern identification accuracy Publication date: 1 October 2019 Source: NeuroImage, Volume 199 Author(s): Natalie E. Hansen, Assaf Harel, Nandini Iyer, Brian D. Simpson, Matthew G. Wisniewski Abstract Recent studies show that pre-stimulus band-specific power and phase in the electroencephalogram (EEG) can predict accuracy on tasks involving the detection of near-threshold stimuli. However, results in the auditory modality have been mixed, and few works have examined pre-stimulus features when more complex decisions are made (e.g. identifying supra-threshold sounds). Further, most auditory studies have used background sounds known to induce oscillatory EEG states, leaving it unclear whether phase predicts accuracy without such background sounds. To address this gap in knowledge, the present study examined pre-stimulus EEG as it relates to accuracy in a tone pattern identification task. On each trial, participants heard a triad of 40-ms sinusoidal tones (separated by 40-ms intervals), one of which was at a different frequency than the other two. Participants' task was to indicate the tone pattern (low-low-high, low-high-low, etc.). No background sounds were employed. Using a phase opposition measure based on inter-trial phase consistencies, pre-stimulus 7–10 Hz phase was found to differ between correct and incorrect trials ∼200 to 100 ms prior to tone-pattern onset. After sorting trials into bins based on phase, accuracy was found to be lowest at around π−+ relative to individuals' most accurate phase bin. No significant effects were found for pre-stimulus power. In the context of the literature, findings suggest an important relationship between the complexity of task demands and pre-stimulus activity within the auditory domain. Results also raise interesting questions about the role of induced oscillatory states or rhythmic processing modes in obtaining pre-stimulus effects of phase in auditory tasks.

Optimization of graph construction can significantly increase the power of structural brain network studies Publication date: 1 October 2019 Source: NeuroImage, Volume 199 Author(s): Eirini Messaritaki, Stavros I. Dimitriadis, Derek K. Jones Abstract Structural brain networks derived from diffusion magnetic resonance imaging data have been used extensively to describe the human brain, and graph theory has allowed quantification of their network properties. Schemes used to construct the graphs that represent the structural brain networks differ in the metrics they use as edge weights and the algorithms they use to define the network topologies. In this work, twenty graph construction schemes were considered. The schemes use the number of streamlines, the fractional anisotropy, the mean diffusivity or other attributes of the tracts to define the edge weights, and either an absolute threshold or a data-driven algorithm to define the graph topology. The test-retest data of the Human Connectome Project were used to compare the reproducibility of the graphs and their various attributes (edges, topologies, graph theoretical metrics) derived through those schemes, for diffusion images acquired with three different diffusion weightings. The impact of the scheme on the statistical power of the study and on the number of participants required to detect a difference between populations or an effect of an intervention was also calculated. The reproducibility of the graphs and their attributes depended heavily on the graph construction scheme. Graph reproducibility was higher for schemes that used thresholding to define the graph topology, while data-driven schemes performed better at topology reproducibility (mean similarities of 0.962 and 0.984 respectively, for graphs derived from diffusion images with b=2000 s/mm2). Additionally, schemes that used thresholding resulted in better reproducibility for local graph theoretical metrics (intra-class correlation coefficients (ICC) of the order of 0.8), compared to data-driven schemes. Thresholded and data-driven schemes resulted in high (0.86 or higher) ICCs only for schemes that use exclusively the number of streamlines to construct the graphs. Crucially, the number of participants required to detect a difference between populations or an effect of an intervention could change by a factor of two or more depending on the scheme used, affecting the power of studies to reveal the effects of interest.

Alexandros Sfakianakis
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