Image classification and reconstruction from low-density EEG.

Recent advances in visual decoding have enabled the classification and reconstruction of perceived images from the brain. However, previous approaches have predominantly relied on stationary, costly equipment like fMRI or high-density EEG, limiting the real-world availability and applicability of such projects. Additionally, several EEG-based paradigms have utilized artifactual, rather than stimulus-related information yielding flawed classification and reconstruction results. Our goal was to reduce the cost of the decoding paradigm, while increasing its flexibility. Therefore, we investigated whether the classification of an image category and the reconstruction of the image itself is possible from the visually evoked brain activity measured by a portable, 8-channel EEG. To compensate for the low electrode count and to avoid flawed predictions, we designed a theory-guided EEG setup and created a new experiment to obtain a dataset from 9 subjects. We compared five contemporary classification models with our setup reaching an average accuracy of 34.4% for 20 image classes on hold-out test recordings. For the reconstruction, the top-performing model was used as an EEG-encoder which was combined with a pretrained latent diffusion model via double-conditioning. After fine-tuning, we reconstructed images from the test set with a 1000 trial 50-class top-1 accuracy of 35.3%. While not reaching the same performance as MRI-based paradigms on unseen stimuli, our approach greatly improved the affordability and mobility of the visual decoding technology.

Get out of my head: social evaluative brain states carry over into post-feedback rest and influence remembering how others view us.

Learning how others perceive us helps us tune our behavior to form adaptive relationships. But which perceptions stick with us? And when in the learning process are they codified in memory? We leveraged a popular television series-The Office-to answer these questions. Prior to their functional magnetic resonance imaging (fMRI) session, viewers of The Office reported which characters they identified with, as well as which characters they perceived another person (i.e. counterpart) was similar to. During their fMRI scan, participants found out which characters other people thought they and the counterpart were like, and also completed rest scans. Participants remembered more feedback inconsistent with their self-views (vs. views of the counterpart). Although neural activity while encoding self-inconsistent feedback did not meaningfully predict memory, returning to the inconsistent self feedback during subsequent rest did. During rest, participants reinstated neural patterns engaged while receiving self-inconsistent feedback in the dorsomedial prefrontal cortex (DMPFC). DMPFC reinstatement also quadratically predicted self-inconsistent memory, with too few or too many reinstatements compromising memory performance. Processing social feedback during rest may impact how we remember and integrate the feedback, especially when it contradicts our self-views.

Effects of endogenous testosterone on oscillatory activity during verbal working memory in youth.

Testosterone levels sharply rise during the transition from childhood to adolescence and these changes are known to be associated with changes in human brain structure. During this same developmental window, there are also robust changes in the neural oscillatory dynamics serving verbal working memory processing. Surprisingly, whereas many studies have investigated the effects of chronological age on the neural oscillations supporting verbal working memory, none have probed the impact of endogenous testosterone levels during this developmental period. Using a sample of 89 youth aged 6-14 years-old, we collected salivary testosterone samples and recorded magnetoencephalography during a modified Sternberg verbal working memory task. Significant oscillatory responses were identified and imaged using a beamforming approach and the resulting maps were subjected to whole-brain ANCOVAs examining the effects of testosterone and sex, controlling for age, during verbal working memory encoding and maintenance. Our primary results indicated robust testosterone-related effects in theta (4-7 Hz) and alpha (8-14 Hz) oscillatory activity, controlling for age. During encoding, females exhibited weaker theta oscillations than males in right cerebellar cortices and stronger alpha oscillations in left temporal cortices. During maintenance, youth with greater testosterone exhibited weaker alpha oscillations in right parahippocampal and cerebellar cortices, as well as regions across the left-lateralized language network. These results extend the existing literature on the development of verbal working memory processing by showing region and sex-specific effects of testosterone, and are the first results to link endogenous testosterone levels to the neural oscillatory activity serving verbal working memory, above and beyond the effects of chronological age.

Shared functional specialization in transformer-based language models and the human brain.

When processing language, the brain is thought to deploy specialized computations to construct meaning from complex linguistic structures. Recently, artificial neural networks based on the Transformer architecture have revolutionized the field of natural language processing. Transformers integrate contextual information across words via structured circuit computations. Prior work has focused on the internal representations ("embeddings") generated by these circuits. In this paper, we instead analyze the circuit computations directly: we deconstruct these computations into the functionally-specialized "transformations" that integrate contextual information across words. Using functional MRI data acquired while participants listened to naturalistic stories, we first verify that the transformations account for considerable variance in brain activity across the cortical language network. We then demonstrate that the emergent computations performed by individual, functionally-specialized "attention heads" differentially predict brain activity in specific cortical regions. These heads fall along gradients corresponding to different layers and context lengths in a low-dimensional cortical space.

An rs-fMRI based neuroimaging marker for adult absence epilepsy.

OBJECTIVE: Approximately 20-30 % of epilepsy patients exhibit negative findings on routine magnetic resonance imaging, and this condition is known as nonlesional epilepsy. Absence epilepsy (AE) is a prevalent form of nonlesional epilepsy. This study aimed to investigate the clinical diagnostic utility of regional homogeneity (ReHo) assessed through the support vector machine (SVM) approach for identifying AE. METHODS: This research involved 102 healthy individuals and 93 AE patients. Resting-state functional magnetic resonance imaging was employed for data acquisition in all participants. ReHo analysis, coupled with SVM methodology, was utilized for data processing. RESULTS: Compared to healthy control individuals, AE patients demonstrated significantly elevated ReHo values in the bilateral putamen, accompanied by decreased ReHo in the bilateral thalamus. SVM was used to differentiate patients with AE from healthy control individuals based on rs-fMRI data. A composite assessment of altered ReHo in the left putamen and left thalamus yielded the highest accuracy at 81.64 %, with a sensitivity of 95.41 % and a specificity of 69.23 %. SIGNIFICANCE: According to the results, altered ReHo values in the bilateral putamen and thalamus could serve as neuroimaging markers for AE, offering objective guidance for its diagnosis.

Large-scale parameters framework with large convolutional kernel for encoding visual fMRI activity information.

Visual encoding models often use deep neural networks to describe the brain's visual cortex response to external stimuli. Inspired by biological findings, researchers found that large receptive fields built with large convolutional kernels improve convolutional encoding model performance. Inspired by scaling laws in recent years, this article investigates the performance of large convolutional kernel encoding models on larger parameter scales. This paper proposes a large-scale parameters framework with a sizeable convolutional kernel for encoding visual functional magnetic resonance imaging activity information. The proposed framework consists of three parts: First, the stimulus image feature extraction module is constructed using a large-kernel convolutional network while increasing channel numbers to expand the parameter size of the framework. Second, enlarging the input data during the training stage through the multi-subject fusion module to accommodate the increase in parameters. Third, the voxel mapping module maps from stimulus image features to functional magnetic resonance imaging signals. Compared to sizeable convolutional kernel visual encoding networks with base parameter scale, our visual encoding framework improves by approximately 7% on the Natural Scenes Dataset, the dedicated dataset for the Algonauts 2023 Challenge. We further analyze that our encoding framework made a trade-off between encoding performance and trainability. This paper confirms that expanding parameters in visual coding can bring performance improvements.

Gender and age related brain structural and functional alterations in children with autism spectrum disorder.

To explore the effects of age and gender on the brain in children with autism spectrum disorder using magnetic resonance imaging. 185 patients with autism spectrum disorder and 110 typically developing children were enrolled. In terms of gender, boys with autism spectrum disorder had increased gray matter volumes in the insula and superior frontal gyrus and decreased gray matter volumes in the inferior frontal gyrus and thalamus. The brain regions with functional alterations are mainly distributed in the cerebellum, anterior cingulate gyrus, postcentral gyrus, and putamen. Girls with autism spectrum disorder only had increased gray matter volumes in the right cuneus and showed higher amplitude of low-frequency fluctuation in the paracentral lobule, higher regional homogeneity and degree centrality in the calcarine fissure, and greater right frontoparietal network-default mode network connectivity. In terms of age, preschool-aged children with autism spectrum disorder exhibited hypo-connectivity between and within auditory network, somatomotor network, and visual network. School-aged children with autism spectrum disorder showed increased gray matter volumes in the rectus gyrus, superior temporal gyrus, insula, and suboccipital gyrus, as well as increased amplitude of low-frequency fluctuation and regional homogeneity in the calcarine fissure and precentral gyrus and decreased in the cerebellum and anterior cingulate gyrus. The hyper-connectivity between somatomotor network and left frontoparietal network and within visual network was found. It is essential to consider the impact of age and gender on the neurophysiological alterations in autism spectrum disorder children when analyzing changes in brain structure and function.

MRI resting-state signature of the propensity to experience meaningful coincidences: a functional coupling analysis.

The propensity to experience meaningful patterns in random arrangements and unrelated events shows considerable interindividual differences. Reduced inhibitory control (over sensory processes) and decreased working memory capacities are associated with this trait, which implies that the activation of frontal as well as posterior brain regions may be altered during rest and working memory tasks. In addition, people experiencing more meaningful coincidences showed reduced gray matter of the left inferior frontal gyrus (IFG), which is linked to the inhibition of irrelevant information in working memory and the control and integration of multisensory information. To study deviations in the functional connectivity of the IFG with posterior associative areas, the present study investigated the fMRI resting state in a large sample of n = 101 participants. We applied seed-to-voxel analysis and found that people who perceive more meaningful coincidences showed negative functional connectivity of the left IFG (i.e. pars triangularis) with areas of the left posterior associative cortex (e.g. superior parietal cortex). A data-driven multivoxel pattern analysis further indicated that functional connectivity of a cluster located in the right cerebellum with a cluster including parts of the left middle frontal gyrus, left precentral gyrus, and the left IFG (pars opercularis) was associated with meaningful coincidences. These findings add evidence to the neurocognitive foundations of the propensity to experience meaningful coincidences, which strengthens the idea that deviations of working memory functions and inhibition of sensory and motor information explain why people experience more meaning in meaningless noise.

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.

Morphological patterns and spatial probability maps of the inferior frontal sulcus in the human brain.

The inferior frontal sulcus (ifs) is a prominent sulcus on the lateral frontal cortex, separating the middle frontal gyrus from the inferior frontal gyrus. The morphology of the ifs can be difficult to distinguish from adjacent sulci, which are often misidentified as continuations of the ifs. The morphological variability of the ifs and its relationship to surrounding sulci were examined in 40 healthy human subjects (i.e., 80 hemispheres). The sulci were identified and labeled on the native cortical surface meshes of individual subjects, permitting proper intra-sulcal assessment. Two main morphological patterns of the ifs were identified across hemispheres: in Type I, the ifs was a single continuous sulcus, and in Type II, the ifs was discontinuous and appeared in two segments. The morphology of the ifs could be further subdivided into nine subtypes based on the presence of anterior and posterior sulcal extensions. The ifs was often observed to connect, either superficially or completely, with surrounding sulci, and seldom appeared as an independent sulcus. The spatial variability of the ifs and its various morphological configurations were quantified in the form of surface spatial probability maps which are made publicly available in the standard fsaverage space. These maps demonstrated that the ifs generally occupied a consistent position across hemispheres and across individuals. The normalized mean sulcal depths associated with the main morphological types were also computed. The present study provides the first detailed description of the ifs as a sulcal complex composed of segments and extensions that can be clearly differentiated from adjacent sulci. These descriptions, together with the spatial probability maps, are critical for the accurate identification of the ifs in anatomical and functional neuroimaging studies investigating the structural characteristics and functional organization of this region in the human brain.

Sex-specific alterations in functional connectivity and network topology in patients with degenerative cervical myelopathy.

Patients with degenerative cervical myelopathy (DCM) experience structural and functional brain reorganization. However, few studies have investigated the influence of sex on cerebral alterations. The present study investigates the role of sex on brain functional connectivity (FC) and global network topology in DCM and healthy controls (HCs). The resting-state functional MRI data was acquired for 100 patients (58 males vs. 42 females). ROI-to-ROI FC and network topological features were characterized for each patient and HC. Group differences in FC and network topological features were examined. Compared to healthy counterparts, DCM males exhibited higher FC between vision-related brain regions, and cerebellum, brainstem, and thalamus, but lower FC between the intracalcarine cortex and frontal and somatosensory cortices, while DCM females demonstrated higher FC between the thalamus and cerebellar and sensorimotor regions, but lower FC between sensorimotor and visual regions. DCM males displayed higher FC within the cerebellum and between the posterior cingulate cortex (PCC) and vision-related regions, while DCM females displayed higher FC between frontal regions and the PCC, cerebellum, and visual regions. Additionally, DCM males displayed significantly greater intra-network connectivity and efficiency compared to healthy counterparts. Results from the present study imply sex-specific supraspinal functional alterations occur in patients with DCM.

EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy.

Electro/Magneto-EncephaloGraphy (EEG/MEG) source imaging (EMSI) of epileptic activity from deep generators is often challenging due to the higher sensitivity of EEG/MEG to superficial regions and to the spatial configuration of subcortical structures. We previously demonstrated the ability of the coherent Maximum Entropy on the Mean (cMEM) method to accurately localize the superficial cortical generators and their spatial extent. Here, we propose a depth-weighted adaptation of cMEM to localize deep generators more accurately. These methods were evaluated using realistic MEG/high-density EEG (HD-EEG) simulations of epileptic activity and actual MEG/HD-EEG recordings from patients with focal epilepsy. We incorporated depth-weighting within the MEM framework to compensate for its preference for superficial generators. We also included a mesh of both hippocampi, as an additional deep structure in the source model. We generated 5400 realistic simulations of interictal epileptic discharges for MEG and HD-EEG involving a wide range of spatial extents and signal-to-noise ratio (SNR) levels, before investigating EMSI on clinical HD-EEG in 16 patients and MEG in 14 patients. Clinical interictal epileptic discharges were marked by visual inspection. We applied three EMSI methods: cMEM, depth-weighted cMEM and depth-weighted minimum norm estimate (MNE). The ground truth was defined as the true simulated generator or as a drawn region based on clinical information available for patients. For deep sources, depth-weighted cMEM improved the localization when compared to cMEM and depth-weighted MNE, whereas depth-weighted cMEM did not deteriorate localization accuracy for superficial regions. For patients' data, we observed improvement in localization for deep sources, especially for the patients with mesial temporal epilepsy, for which cMEM failed to reconstruct the initial generator in the hippocampus. Depth weighting was more crucial for MEG (gradiometers) than for HD-EEG. Similar findings were found when considering depth weighting for the wavelet extension of MEM. In conclusion, depth-weighted cMEM improved the localization of deep sources without or with minimal deterioration of the localization of the superficial sources. This was demonstrated using extensive simulations with MEG and HD-EEG and clinical MEG and HD-EEG for epilepsy patients.

How sex and gender shape functional brain networks.

Sex and gender differences exist in the prevalence and clinical manifestation of common brain disorders. Identifying their neural correlates may help improve clinical care.

Newborn's neural representation of instrumental and vocal music as revealed by fMRI: A dynamic effective brain connectivity study.

Music is ubiquitous, both in its instrumental and vocal forms. While speech perception at birth has been at the core of an extensive corpus of research, the origins of the ability to discriminate instrumental or vocal melodies is still not well investigated. In previous studies comparing vocal and musical perception, the vocal stimuli were mainly related to speaking, including language, and not to the non-language singing voice. In the present study, to better compare a melodic instrumental line with the voice, we used singing as a comparison stimulus, to reduce the dissimilarities between the two stimuli as much as possible, separating language perception from vocal musical perception. In the present study, 45 newborns were scanned, 10 full-term born infants and 35 preterm infants at term-equivalent age (mean gestational age at test = 40.17 weeks, SD = 0.44) using functional magnetic resonance imaging while listening to five melodies played by a musical instrument (flute) or sung by a female voice. To examine the dynamic task-based effective connectivity, we employed a psychophysiological interaction of co-activation patterns (PPI-CAPs) analysis, using the auditory cortices as seed region, to investigate moment-to-moment changes in task-driven modulation of cortical activity during an fMRI task. Our findings reveal condition-specific, dynamically occurring patterns of co-activation (PPI-CAPs). During the vocal condition, the auditory cortex co-activates with the sensorimotor and salience networks, while during the instrumental condition, it co-activates with the visual cortex and the superior frontal cortex. Our results show that the vocal stimulus elicits sensorimotor aspects of the auditory perception and is processed as a more salient stimulus while the instrumental condition activated higher-order cognitive and visuo-spatial networks. Common neural signatures for both auditory stimuli were found in the precuneus and posterior cingulate gyrus. Finally, this study adds knowledge on the dynamic brain connectivity underlying the newborns capability of early and specialized auditory processing, highlighting the relevance of dynamic approaches to study brain function in newborn populations.

Distinct dynamic connectivity profiles promote enhanced conscious perception of auditory stimuli.

The neuroscience of consciousness aims to identify neural markers that distinguish brain dynamics in healthy individuals from those in unconscious conditions. Recent research has revealed that specific brain connectivity patterns correlate with conscious states and diminish with loss of consciousness. However, the contribution of these patterns to shaping conscious processing remains unclear. Our study investigates the functional significance of these neural dynamics by examining their impact on participants' ability to process external information during wakefulness. Using fMRI recordings during an auditory detection task and rest, we show that ongoing dynamics are underpinned by brain patterns consistent with those identified in previous research. Detection of auditory stimuli at threshold is specifically improved when the connectivity pattern at stimulus presentation corresponds to patterns characteristic of conscious states. Conversely, the occurrence of these conscious state-associated patterns increases after detection, indicating a mutual influence between ongoing brain dynamics and conscious perception. Our findings suggest that certain brain configurations are more favorable to the conscious processing of external stimuli. Targeting these favorable patterns in patients with consciousness disorders may help identify windows of greater receptivity to the external world, guiding personalized treatments.

Dissonant music engages early visual processing.

The neuroscientific examination of music processing in audio-visual contexts offers a valuable framework to assess how auditory information influences the emotional encoding of visual information. Using fMRI during naturalistic film viewing, we investigated the neural mechanisms underlying the effect of music on valence inferences during mental state attribution. Thirty-eight participants watched the same short-film accompanied by systematically controlled consonant or dissonant music. Subjects were instructed to think about the main character's intentions. The results revealed that increasing levels of dissonance led to more negatively valenced inferences, displaying the profound emotional impact of musical dissonance. Crucially, at the neuroscientific level and despite music being the sole manipulation, dissonance evoked the response of the primary visual cortex (V1). Functional/effective connectivity analysis showed a stronger coupling between the auditory ventral stream (AVS) and V1 in response to tonal dissonance and demonstrated the modulation of early visual processing via top-down feedback inputs from the AVS to V1. These V1 signal changes indicate the influence of high-level contextual representations associated with tonal dissonance on early visual cortices, serving to facilitate the emotional interpretation of visual information. Our results highlight the significance of employing systematically controlled music, which can isolate emotional valence from the arousal dimension, to elucidate the brain's sound-to-meaning interface and its distributive crossmodal effects on early visual encoding during naturalistic film viewing.

Functional brain networks are associated with both sex and gender in children.

Sex and gender are associated with human behavior throughout the life span and across health and disease, but whether they are associated with similar or distinct neural phenotypes is unknown. Here, we demonstrate that, in children, sex and gender are uniquely reflected in the intrinsic functional connectivity of the brain. Somatomotor, visual, control, and limbic networks are preferentially associated with sex, while network correlates of gender are more distributed throughout the cortex. These results suggest that sex and gender are irreducible to one another not only in society but also in biology.

Task and stimulus coding in the multiple-demand network.

In the human brain, a multiple-demand (MD) network plays a key role in cognitive control, with core components in lateral frontal, dorsomedial frontal and lateral parietal cortex, and multivariate activity patterns that discriminate the contents of many cognitive activities. In prefrontal cortex of the behaving monkey, different cognitive operations are associated with very different patterns of neural activity, while details of a particular stimulus are encoded as small variations on these basic patterns (Sigala et al, 2008). Here, using the advanced fMRI methods of the Human Connectome Project and their 360-region cortical parcellation, we searched for a similar result in MD activation patterns. In each parcel, we compared multivertex patterns for every combination of three tasks (working memory, task-switching, and stop-signal) and two stimulus classes (faces and buildings). Though both task and stimulus category were discriminated in every cortical parcel, the strength of discrimination varied strongly across parcels. The different cognitive operations of the three tasks were strongly discriminated in MD regions. Stimulus categories, in contrast, were most strongly discriminated in a large region of primary and higher visual cortex, and intriguingly, in both parietal and frontal lobe regions adjacent to core MD regions. In the monkey, frontal neurons show a strong pattern of nonlinear mixed selectivity, with activity reflecting specific conjunctions of task events. In our data, however, there was limited evidence for mixed selectivity; throughout the brain, discriminations of task and stimulus combined largely linearly, with a small nonlinear component. In MD regions, human fMRI data recapitulate some but not all aspects of electrophysiological data from nonhuman primates.

Neural dysfunction underlying working memory processing at different stages of the illness course in schizophrenia: a comparative meta-analysis.

Schizophrenia, as a chronic and persistent disorder, exhibits working memory deficits across various stages of the disorder, yet the neural mechanisms underlying these deficits remain elusive with inconsistent neuroimaging findings. We aimed to compare the brain functional changes of working memory in patients at different stages: clinical high risk, first-episode psychosis, and long-term schizophrenia, using meta-analyses of functional magnetic resonance imaging studies. Following a systematic literature search, 56 whole-brain task-based functional magnetic resonance imaging studies (15 for clinical high risk, 16 for first-episode psychosis, and 25 for long-term schizophrenia) were included. The separate and pooled neurofunctional mechanisms among clinical high risk, first-episode psychosis, and long-term schizophrenia were generated by Seed-based d Mapping toolbox. The clinical high risk and first-episode psychosis groups exhibited overlapping hypoactivation in the right inferior parietal lobule, right middle frontal gyrus, and left superior parietal lobule, indicating key lesion sites in the early phase of schizophrenia. Individuals with first-episode psychosis showed lower activation in left inferior parietal lobule than those with long-term schizophrenia, reflecting a possible recovery process or more neural inefficiency. We concluded that SCZ represent as a continuum in the early stage of illness progression, while the neural bases are inversely changed with the development of illness course to long-term course.

Empathy incites a stable prosocial decision bias.

Empathy toward suffering individuals serves as potent driver for prosocial behavior. However, it remains unclear whether prosociality induced by empathy for another person's pain persists once that person's suffering diminishes. To test this, participants underwent functional magnetic resonance imaging while performing a binary social decision task that involved allocation of points to themselves and another person. In block one, participants completed the task after witnessing frequent painful stimulation of the other person, and in block two, after observing low frequency of painful stimulation. Drift-diffusion modeling revealed an increased initial bias toward making prosocial decisions in the first block compared with baseline that persisted in the second block. These results were replicated in an independent behavioral study. An additional control study showed that this effect may be specific to empathy as stability was not evident when prosocial decisions were driven by a social norm such as reciprocity. Increased neural activation in dorsomedial prefrontal cortex was linked to empathic concern after witnessing frequent pain and to a general prosocial decision bias after witnessing rare pain. Altogether, our findings show that empathy for pain elicits a stable inclination toward making prosocial decisions even as their suffering diminishes.