Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Microstructural Correlates of Emotional Attribution Impairment in Non-Demented Patients with Amyotrophic Lateral Sclerosis

  • Chiara Crespi ,

    crespi.chiara@hsr.it

    Affiliations Università Vita-Salute San Raffaele, Milano, Italy, Division of Neuroscience, San Raffaele Scientific Institute, Milano, Italy

  • Chiara Cerami,

    Affiliations Università Vita-Salute San Raffaele, Milano, Italy, Division of Neuroscience, San Raffaele Scientific Institute, Milano, Italy, Department of Clinical Neurosciences, IRCCS San Raffaele Turro, Milano, Italy

  • Alessandra Dodich,

    Affiliations Università Vita-Salute San Raffaele, Milano, Italy, Division of Neuroscience, San Raffaele Scientific Institute, Milano, Italy

  • Nicola Canessa,

    Affiliations Division of Neuroscience, San Raffaele Scientific Institute, Milano, Italy, NeTS Center, Scuola Universitaria Superiore IUSS, Pavia, Italy

  • Sandro Iannaccone,

    Affiliations Division of Neuroscience, San Raffaele Scientific Institute, Milano, Italy, Department of Clinical Neurosciences, IRCCS San Raffaele Turro, Milano, Italy

  • Massimo Corbo,

    Affiliation Department of Neurorehabilitation Sciences, Casa Cura Policlinico, Milano, Italy

  • Christian Lunetta,

    Affiliation NEuroMuscolar Omnicentre, Fondazione Serena Onlus, Niguarda Ca’ Granda Hospital, Milano, Italy

  • Andrea Falini,

    Affiliations Università Vita-Salute San Raffaele, Milano, Italy, Division of Neuroscience, San Raffaele Scientific Institute, Milano, Italy, CERMAC – Neuroradiology, San Raffaele Scientific Institute, Milano, Italy

  • Stefano F. Cappa

    Affiliations Division of Neuroscience, San Raffaele Scientific Institute, Milano, Italy, NeTS Center, Scuola Universitaria Superiore IUSS, Pavia, Italy

Abstract

Impairments in the ability to recognize and attribute emotional states to others have been described in amyotrophic lateral sclerosis patients and linked to the dysfunction of key nodes of the emotional empathy network. Microstructural correlates of such disorders are still unexplored. We investigated the white-matter substrates of emotional attribution deficits in a sample of amyotrophic lateral sclerosis patients without cognitive decline. Thirteen individuals with either probable or definite amyotrophic lateral sclerosis and 14 healthy controls were enrolled in a Diffusion Tensor Imaging study and administered the Story-based Empathy Task, assessing the ability to attribute mental states to others (i.e., Intention and Emotion attribution conditions). As already reported, a significant global reduction of empathic skills, mainly driven by a failure in Emotion Attribution condition, was found in amyotrophic lateral sclerosis patients compared to healthy subjects. The severity of this deficit was significantly correlated with fractional anisotropy along the forceps minor, genu of corpus callosum, right uncinate and inferior fronto-occipital fasciculi. The involvement of frontal commissural fiber tracts and right ventral associative fronto-limbic pathways is the microstructural hallmark of the impairment of high-order processing of socio-emotional stimuli in amyotrophic lateral sclerosis. These results support the notion of the neurofunctional and neuroanatomical continuum between amyotrophic lateral sclerosis and frontotemporal dementia.

Introduction

Amyotrophic Lateral Sclerosis (ALS) is a heterogeneous multi-component disease, including in about 50% of cases non-motor dysfunctions encompassing the cognitive and/or behavioral changes typically observed in the behavioral variant of frontotemporal dementia (bvFTD) [1]. More specifically, such extra-motor impairments can range from the identification of isolate deficits to the fulfillment of the criteria for the diagnosis of frontotemporal dementia (FTD), and particularly of the bvFTD syndrome [23]. The latter condition affects about 10–15% of ALS cases [46]. Notably, an overlap between the two conditions has also been observed at genetic and neuropathological levels, supporting the ALS-FTD continuum hypothesis [710]. Neuroimaging findings provided further suggestive evidence, by showing the pathological involvement of frontal and temporo-limbic regions in ALS patients [1113].

Cognitive extra-motor impairments in ALS may involve language, memory and executive abilities [4, 6, 14], while the major behavioral symptom is apathy [1517]. Many studies also underlined a set of alterations specifically related to the failure of social cognition skills, i.e. one of the core features of the bvFTD syndrome [3], and social cognition deficit is now recognized as one of the main cognitive signatures possibly occurring in ALS [18]. In particular, social cognition impairments include a constellation of dysfunctions ranging from the simple processing of socio-emotional stimuli–e.g., identification of basic emotions from facial expression [1920], social judgments and memory for emotional material [2123], affective decision-making [2425]–to more complex functions such as empathy [2628], a psychological construct reflecting the overall ability to attribute mental states to others in order to understand their behavior in the social context.

Empathy is usually broken down into two main components [29]. The first one relies on the production of a visceromotor representation of affective states of others, allowing the experience of their feelings (i.e., emotional contagion or emotional empathy). The second aspect is the computation of others’ cognitive states and intentions (i.e., perspective-taking/mentalizing skills or cognitive empathy). At the neural level, while the cognitive facet of empathy engages a temporo-parietal network (i.e., superior temporal sulcus, temporo-parietal junction, medial prefrontal cortex), emotional attribution abilities require a distinct system, involving a set of fronto-limbic regions (i.e., inferior frontal gyrus, anterior insula, anterior cingulate cortex) [3031].

Given the early and progressive degeneration of frontal and limbic structures in bvFTD patients [3233], the impairment of components of social cognition skills in these subjects is not unexpected [3437], and indeed represents a key feature of the disease [3]. There is now a growing evidence of deficits in the recognition and processing of socio-emotional information in ALS [18], compatible with an overlap between ALS and FTD. Indeed, such impairments show common structural and functional underpinnings in the two clinical syndromes [18, 38]. Impaired performance in a task assessing emotional attribution have been reported in both ALS and bvFTD [27, 36, 39]. This was related to macrostructural changes in fronto-limbic structures representing key nodes of the neural system supporting emotional empathy [27, 36]. To date, no study specifically explored the microstructural white-matter correlates of the processing of emotional stimuli in ALS. The assessment of brain connectivity within the fronto-limbic circuitry with the Diffusion Tensor Imaging (DTI) techniques may offer useful suggestions about the impairment of specific social cognition networks in ALS and increase our comprehension of the pathological ALS-FTD continuum.

Materials and Methods

Participants

Thirteen subjects with a diagnosis of either probable or definite ALS [40] who did not have dementia (10 males and 3 females, mean age = 58.97±10.57 years; mean education = 11.5±4.41 years), and 14 age-, gender- and education-matched healthy controls (HC) were recruited in the DTI study. The patients were a subsample of the group reported by Cerami and colleagues [27]. See Table 1 for demographic and clinical details.

thumbnail
Table 1. Demographic information and clinical characteristics.

https://doi.org/10.1371/journal.pone.0161034.t001

ALS patients underwent a structured clinical interview, a full neurological examination, and a Magnetic Resonance Imaging (MRI) investigation, including T1, T2, and FLAIR sequences, performed for diagnostic purposes. All ALS cases had no known family history. Patients were routinely screened for pathogenic mutations on C9ORF72 and GRN genes. No patient carried known mutations. Exclusion criteria were left-handedness, a positive history for other neurological or psychiatric disorders, and the presence of other pathological evidence on MRI scan. We also excluded patients with respiratory disorders (forced vital capacity <70% of predicted capacity), severe dysarthria and communication difficulties potentially invalidating the interpretation of neuropsychological performances. According to the disease-onset type, 3 patients out of 13 had a bulbar-onset disease (i.e., dysarthria and dysphagia), while the rest presented with spinal-onset. Patients’ disability was evaluated with the revised version of the ALS-Functional Rating Scale (ALSFRS-r) [41].

All ALS subjects completed a standard neuropsychological evaluation to assess the presence of cognitive and/or behavioral impairments. Specifically, we assessed language (picture naming and single word comprehension), memory (verbal working-memory: digit span forward; long-term memory: Rey Auditory Verbal Learning test), and executive functions (Raven Colored Progressive Matrices; digit span backward; letter and category fluency tests; Cognitive Estimation Task; Stroop interference test and either Wisconsin Card Sorting Test or Weigl’s Sorting Test), as well as the presence of behavioral dysfunctions (Frontal Behavioral Inventory and Neuropsychiatric Inventory). On the basis of the presence/absence of cognitive and/or behavioral impairments, and according to Strong’s consensus criteria [42], we identified 2 ALS patients with dysexecutive deficits (i.e., ALSci) and 2 additional patients with behavioral disorders (i.e., apathy, irritability and disinhibition, namely ALSbi). No patient with combined ALSci/bi syndrome was identified. The remaining patients (i.e., 9/13, 70%) were cognitively and behaviorally unimpaired (i.e., pure ALS).

Cognitively normal subjects (HC) were recruited from local senior community centers. Inclusion criteria were the absence of neuropsychiatric disorders, negative neurologic examination, global Clinical Dementia Rating score = 0, Mini-Mental State Examination score ≥28/30, verbal and visuo-spatial delayed memory performance (Rey Auditory Verbal Learning test and Rey Figure Recall task) ≥25th percentile. None of the HC was taking any medication potentially interfering with neurobehavioral functioning. A next of kin (e.g., spouse) of each control subject was interviewed to corroborate his/her normal daily functioning.

All subjects or relative informants gave their written informed consent to the experimental procedure, which was approved by the Ethical Committee of San Raffaele Hospital.

Story-based Empathy Task

ALS and HC were compared on the basis of the performance on the Story-based Empathy Task (SET), a non-verbal test targeting individuals’ ability to correctly attribute mental states to other agents [27, 36, 43]. Experimental procedure and stimuli were derived from tasks used in previous studies [4446] and has been described for the first time in Cerami et al. [27]. Briefly, the SET includes two experimental conditions, namely the Emotion Attribution (EA) and Intention Attribution (IA) conditions, requiring to infer emotional and intentional states, respectively. The test also includes a control condition (Causal Inference or CI condition), involving the comprehension of physical cause-effect relationships.

Every condition includes six strips depicting different stories, and gives a maximum score of 6 for each condition. The task requires the interpretation of specific story-content for each strip. Firstly, three drawings describing consecutive moments of a story are presented in the upper half of the screen. So, the subject is asked to describe the story and to formulate a possible story ending. Then, three other drawings showing possible endings (i.e. plausible, implausible, and plausible but incorrect) are presented in random positions across different trials in the lower part of the screen. Finally, the subject is asked to select one of the three possible alternative endings (for further details and an example of the stimuli see [27]). EA condition includes strips based on the attribution of one of the six basic emotions (i.e., anger, fear, disgust, sadness, happiness, surprise). A failure to correctly select the story ending entails a misjudgment on intentions and emotions of the main character on EA and IA conditions, or an erroneous comprehension of causality on CI condition.

Group differences on the global score, as well as on single condition sub-scores, were analyzed with either parametric or nonparametric tests, depending on data distribution.

Diffusion Tensor Imaging (DTI) data acquisition and analysis

All participants underwent Diffusion Tensor Imaging (DTI) on a 3-T Philips Achieva scanner (Philips Medical Systems, Best, NL) with an 8-channel head coil. Whole-brain DTI data were collected using a single-shot echo planar sequence (TR/TE = 8986/80 msec; FOV = 240 mm2; 56 sections; 2.5 mm isotropic resolution) with parallel imaging (SENSE factor, R = 2.5) and diffusion gradients applied along 32 non-collinear directions (b-value = 1000 sec/mm2). One non-diffusion weighted volume was also acquired.

Preprocessing and analysis of DTI data were performed via the FMRIB Software Library (FSL: http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/‎) tools. Single-subject datasets were first corrected for eddy current distortions and motion artifacts, applying a full affine (linear) alignment of each volume to the no-diffusion weighting image. Corrected datasets were skull-stripped and, finally, as a result of the fitting of the diffusion tensor model at each voxel, maps of fractional anisotropy (FA) were generated. Whole-brain group analysis on FA was carried out via Tract-Based Spatial Statistics (TBSS), as described by Smith and colleagues [47]. We first computed a comparison between ALS and HC, and then we correlated whole-brain FA and EA scores in patient group. Voxelwise analyses were performed using Randomise–i.e., a software implemented in FSL performing a permutation-based nonparametric approach within the framework of the GLM–with 10,000 random permutations per contrast, and the Threshold-Free Cluster Enhancement as thresholding method [48]. The t-test comparison between patients and controls was considered significant at p<0.01 uncorrected. The threshold of significance relative to the correlation analysis between whole-brain FA and EA scores in ALS patients was set at p<0.05 corrected for multiple comparisons. The result maps were smoothed applying a Gaussian Kernel of 3 mm via the tbss_fill script. Localization of significant clusters was performed employing the JHU White-Matter Tractography Atlas and the JHU ICBM-DTI-82 White-Matter Labels [49].

Finally, we performed off-line analyses to explore the relationship between behavioral performances and mean FA values extracted from the significant results obtained in the whole-brain correlation analysis. Off-line correlations were computed with Statistica software (https://www.statsoft.com/), and p-values were corrected for multiple comparisons with the False Discovery Rate (FDR) method [50].

Results

SET impairments in non-demented sporadic ALS patients

The results from the whole group, of which these patients are a subsample, have been reported in Cerami et al. [35]. We compared ALS patients with HC on both SET global score and single condition scores. Since the SET scores did not fit a normal distribution (Liliefors test p>0.05), we used non-parametric statistics (Mann-Whitney U Test).

These analyses confirmed a significant failure in patients, compared with HC, in the overall attribution skills, measured with the SET global score (U = 40.50, p = 0.009, Cliff’s delta = 0.55). In order to ensure that this result was not driven by the presence of clinically evident cognitive or behavioral alterations, we repeated the same analysis excluding both ALSci and ALSbi subjects. Group comparison between pure ALS (n = 9) and HC on the SET global performances confirmed the presence of a significant decrease in attribution abilities in patients (U = 28, p = 0.018, Cliff’s delta = 0.55).

We then explored group differences in the EA, IA and CI conditions separately. This analysis confirmed that the emotion attribution condition was the only one impaired in patients (EA condition: U = 49, p = 0.028, Cliff’s delta = 0.46) (Table 2), with 6 out of 13 patients (2 ALSbi, 1 ALSci, 3 pure ALS) performing equal to or below the 5th percentile of the controls’ scores distribution. See also S1 Fig for further details about ALS and HC performances.

thumbnail
Table 2. group differences on mental states attribution abilities.

https://doi.org/10.1371/journal.pone.0161034.t002

Microstructural correlates of emotion attribution impairment in ALS patients

Whole-brain voxelwise comparison on FA images revealed a significant reduction in ALS patients compared to HC involving both left and right corticospinal tract (Fig 1, p<0.01 uncorrected).

thumbnail
Fig 1. TBSS Whole-brain comparison between ALS patients and HC.

Whole-brain comparison between ALS patients and HC showing a significant fractional anisotropy (FA) reduction (p>0.01 uncorrected) along the bilateral corticospinal tract (CST). Statistical map is superimposed to the FMRIB standard-space FA template.

https://doi.org/10.1371/journal.pone.0161034.g001

Whole-brain correlation analysis between skeletonized FA maps and EA scores in ALS patients highlighted a positive correlation (p<0.05 corrected for multiple comparisons) in four clusters localized along the fronto-temporal portions of ventral associative bundles, i.e., right inferior fronto-occipital (IFOF) and uncinate (UF) fasciculi, commissural fiber tracts (forceps minor and genu of the corpus callosum) and in the left superior longitudinal fasciculus (SLF) (Fig 2, Table 3). We then extracted mean FA values from binary maps of significant clusters from both patients and controls FA images. In these clusters, ALS patients showed a 1.8% reduction in diffusion coherence compared to HC, with 4/13 patients having mean FA values below the 5th percentile of HCs’ distribution. We than computed a separate slopes model using FA from significant clusters as predictor of the EA scores. Results highlighted a main effect of group (F(1,23) = 8.86, p = 0.007), and a significant interaction effect between group and mean FA values of significant clusters (F(2, 23) = 14.22, p = 0.0001), with a strong positive correlation in ALS patients (r = 0.84, p = 0.0013) and no correlation in the control group (r = 0.01, p = 0.98).

thumbnail
Fig 2. Correlation between white-matter microstructure and Emotion Attribution (EA) scores in ALS patients.

Significant correlation between microstructural white-matter integrity (i.e., fractional anisotropy index) and EA performances in ALS patients. Statistical maps (p<0.05, FWE-corrected) are superimposed to the FMRIB standard-space FA template.

https://doi.org/10.1371/journal.pone.0161034.g002

thumbnail
Table 3. correlation between white-matter microstructure and Emotion Attribution scores in ALS patients.

https://doi.org/10.1371/journal.pone.0161034.t003

Finally, in order to exclude the possible impact of the high variability of disease duration on the relationship between emotion attribution skills and microstructural integrity in ALS patients (i.e., 25±22.37 months), we computed a partial correlation analysis between EA scores and mean FA values extracted from significant clusters, using the time interval (i.e., months) from symptoms onset as a controlling variable. The correlation coefficients remained significant (p<0.005, FDR corrected) even after controlling for disease duration (Fig 3, Table 3).

thumbnail
Fig 3. Correlation between white-matter microstructure and Emotion Attribution (EA) scores in ALS patients.

The scatterplots illustrates significant correlations between EA scores and FA index within the significant clusters in ALS patients.

https://doi.org/10.1371/journal.pone.0161034.g003

Discussion

The present study explored for the first time the microstructural white-matter correlates of emotion attribution in a sample of non-demented sporadic ALS patients. Our results suggest that an impairment in empathic processing may occur in some cases, even in absence of deficits in other cognitive domains (i.e., pure ALS patients). Such an evidence is in contrast with the hypothesis that social cognition deficits in non-demented ALS patients are a consequence of executive impairment [51], and indicate that the failure in the ability to attribute mental states to another agent may be independent from the presence of additional extra-motor impairments. Notably, the overall attribution deficit emerged in ALS patients was driven by a specific alteration in emotional empathy (i.e., measured with the SET Emotional Attribution condition), suggesting a decline in the ability to identify emotional states in others.

Whole-brain voxelwise comparison on FA between ALS patients and healthy controls failed to find extra-motor microstructural changes on fronto-temporal pathways in our ALS sample, as previously reported [12]. Besides, the inclusion in this work of a smaller ALS patients sample without cognitive decline, a shorter disease duration and lower disease disability scores compared to previous literature [12, 5253] may justify the lack of commissural and frontotemporal white-matter changes.

Conversely, the results of the whole-brain correlation analysis strongly support the notion of an extra-motor fronto-temporal and limbic involvement within the emotional empathy network in ALS. Pathological changes in the microstructural organization of intra- and interhemispheric fiber tracts conveying information between prefrontal, fronto-parietal and temporo-limbic areas may contribute to the decline of emotional empathy skills in ALS patients without cognitive decline in different ways. First, a lower diffusion coherence index along the right ventral bundles (IFOF/UF) may influence the processing of emotional information–i.e., from perception and modulation of the emotional characteristics of the visual percept (IFOF) to the visceromotor reaction to the stimulus (UF) [5455]–by compromising the computations underlying the production of the embodied simulation that prompts the vicarious sharing of another’s feeling [31, 56]. Secondly, a decrease in microstructural integrity along anterior commissural connections (genu of corpus callosum and forceps minor) may interfere with the interhemispheric cooperation mediated by connections of the limbic system, which is crucial for the processing of affective valence of social stimuli. Indeed, coding, evaluation and interpretation of emotional cues within a social context, as well as inferences related to others’ mental states, require the integration of information and the coordination of the processing competences of the two hemispheres [57]. Finally, microstructural features of the SLF have been recently linked to interindividual differences in empathic concern, a measure reflecting emotional empathy [58]. The alteration of the fronto-parietal connectivity subserved by this bundle may thus affect the sensorimotor integration required to recognize and imitate other’s actions, a key mechanism for the development of social cognition abilities, included the expression of empathic responses [59].

Our results provide thus further evidence that white-matter impairment of frontal commissural fiber tracts (i.e., genu of corpus callosum and forceps minor), associative fronto-limbic (UF/IFOF) pathways is a distinctive characteristic of pathological conditions in which changes in the processing of the emotional valence of social stimuli represent a key cognitive feature [6065]. In addition, in agreement with previous evidence showing a pathological disruption of rostral commissural and ventral bundles in ALS patients [12], we proved in our ALS sample the same pattern of white-matter changes that has been proposed as microstructural neuroanatomical marker of the progressive degeneration observed in bvFTD [6668]. Notably, the white-matter bundles we found related to the impairment of empathic skills in ALS patients (i.e., genu of corpus callosum, IFOF, ILF) are those subserving the anatomical connectivity between prefrontal and limbic networks found to be altered in bvFTD resting state activity [39]. Together these findings further support the neurofunctional and neuroanatomical continuum between ALS and bvFTD.

It has been proposed that extra-motor impairments in ALS, including those affecting empathic abilities, might be the result of the involvement of mirror system networks (see [69]). In particular, such an impairment may be due to a breakdown in the automatic ‘resonance’ mechanism leading to a vicarious sharing of another’s feelings by coding a representation based on the bodily state of that agent in a given moment (i.e., embodied simulation) [56]. The neural computations supporting such a basic mechanism of emotional contagion involve both fronto-parietal (SLF) and fronto-limbic networks. The former component, associating action perception and production, modulates the activity of fronto-limbic regions through anatomical connection with the insular cortex. The latter (including the IFG, the ACC and the anterior insula) has been linked to interoceptive awareness and sense of subjectivity [7071], and is engaged in mirror-like effects in both physical and social aversive contexts [72]. Accordingly, previous evidence reported macrostructural changes in core components of the emotional empathy network–namely, the fronto-insular cortex and the ACC–in ALS patients [27], in parallel with bvFTD [36], and the degree of the grey-matter damage highlighted in patients was positively associated to the severity of the impairment in the SET Emotional Attribution condition.

The main limitations of the study are the small sample size, and the lack of assessment of empathic behavior in real life situations. This work is, however, a proof-of-concept study aiming to provide preliminary and exploratory data. No previous data on the correlation between white matter damage and socio-emotional processing disorders in ALS have been so far reported. Although further studies are required to confirm the present findings, our results highlight important implications for clinical practice. The possible occurrence of socio-emotional impairments–and more generally of extra-motor symptoms–in early stages of ALS raises critical issues about patients’ advance directives and the need for effective supporting programs for patient and caregivers. A comprehensive assessment of different facets of non-motor (i.e., behavioral, cognitive and socio-emotional) deficits in ALS is needed to improve diagnostic accuracy and to support caregivers’ training and management strategies.

Supporting Information

S1 Fig. Participants’ performances related to mental attribution abilities.

The figure describes the distribution of participants’ scores (see the color legend on the bottom) in the three SET conditions (y-axis: CI = causal inference, IA = intention attribution, EA = emotion attribution) in patients with amyotrophic lateral sclerosis (ALS) and healthy controls (HC). Values along the x-axis indicate the number of subjects in each group (13 patients and 14 controls).

https://doi.org/10.1371/journal.pone.0161034.s001

(TIF)

Acknowledgments

We wish to thank Alessandra Marcone for the support, Monica Consonni and Stefania Rossi for the neuropsychological assessment of ALS patients.

Author Contributions

  1. Conceived and designed the experiments: CCr CCe SFC.
  2. Performed the experiments: AD NC AF.
  3. Analyzed the data: CCr.
  4. Wrote the paper: CCr CCe AD SFC.
  5. Patient recruitment: SI MC CL.

References

  1. 1. Goldstein LH, Abrahams S. Changes in cognition and behaviour in amyotrophic lateral sclerosis: nature of impairment and implications for assessment. Lancet Neurol. 2013; 12(4): 368–380. pmid:23518330
  2. 2. Neary D, Snowden JS, Gustafson L, Passant U, Stuss D, Black SA, et al. Frontotemporal lobar degeneration A consensus on clinical diagnostic criteria. Neurology. 1998; 51(6): 1546–1554. pmid:9855500
  3. 3. Rascovsky K, Hodges JR, Knopman D, Mendez MF, Kramer JH, Neuhaus J, et al. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain. 2011; 134(9): 2456–2477.
  4. 4. Consonni M, Iannaccone S, Cerami C, Frasson P, Lacerenza M, Lunetta C, et al. The cognitive and behavioural profile of amyotrophic lateral sclerosis: application of the consensus criteria. Behav Neurol. 2013; 27(2): 143–153. pmid:23001631
  5. 5. Lomen-Hoerth C. Clinical phenomenology and neuroimaging correlates in ALS-FTD. J Mol Neurosci. 2011; 45(3): 656–662. pmid:21971978
  6. 6. Phukan J, Elamin M, Bede P, Jordan N, Gallagher L, Byrne S, et al. The syndrome of cognitive impairment in amyotrophic lateral sclerosis: a population-based study. J Neurol Neurosurg Psychiatry. 2012; 83(1): 102–108. pmid:21836033
  7. 7. Boeve BF, Boylan KB, Graff-Radford NR, DeJesus-Hernandez M, Knopman DS, Pedraza O, et al. Characterization of frontotemporal dementia and/or amyotrophic lateral sclerosis associated with the GGGGCC repeat expansion in C9ORF72. Brain. 2012;135(3): 765–783.
  8. 8. Callister JB, Pickering-Brown SM. Pathogenesis/genetics of frontotemporal dementia and how it relates to ALS. Exp Neurol. 2014; 262: 84–90. pmid:24915640
  9. 9. Ling SC, Polymenidou M, Cleveland DW. Converging mechanisms in ALS and FTD: disrupted RNA and protein homeostasis. Neuron. 2013; 79(3): 416–438. pmid:23931993
  10. 10. Renton AE, Chiò A, Traynor BJ. State of play in amyotrophic lateral sclerosis genetics. Nat Neurosci. 2014; 17(1): 17–23. pmid:24369373
  11. 11. Abrahams S, Goldstein LH, Suckling J, Ng V, Simmons A, Chitnis X, et al. Frontotemporal white matter changes in amyotrophic lateral sclerosis. J Neurol. 2005; 252(3): 321–331. pmid:15739047
  12. 12. Lillo P, Mioshi E, Burrell JR, Kiernan MC, Hodges JR, Hornberger M. Grey and white matter changes across the amyotrophic lateral sclerosis-frontotemporal dementia continuum. PLoS One. 2012; 7(8): e43993. pmid:22952843
  13. 13. Tsermentseli S, Leigh PN, Goldstein L. The anatomy of cognitive impairment in amyotrophic lateral sclerosis: more than frontal lobe dysfunction. Cortex. 2012; 48(2): 166–182. pmid:21396632
  14. 14. Consonni M, Rossi S, Cerami C, Marcone A, Iannaccone S, Cappa SF et al. Executive dysfunction affects word list recall performance: Evidence from amyotrophic lateral sclerosis and other neurodegenerative diseases. J Neuropsychol. 2015;
  15. 15. Lillo P, Mioshi E, Zoing MC, Kiernan MC, Hodges JR. How common are behavioural changes in amyotrophic lateral sclerosis?. Amyotroph Lateral Sc. 2011; 12(1): 45–51.
  16. 16. Tsujimoto M, Senda J, Ishihara T, Niimi Y, Kawai Y, Atsuta N, et al. Behavioral changes in early ALS correlate with voxel-based morphometry and diffusion tensor imaging. J Neurol Sci. 2011; 307(1): 34–40.
  17. 17. Woolley SC, Zhang Y, Schuff N, Weiner MW, Katz JS. Neuroanatomical correlates of apathy in ALS using 4 Tesla diffusion tensor MRI. Amyotroph Lateral Sc. 2011; 12(1): 52–58.
  18. 18. Beeldman E, Raaphorst J, Twennaar MK, de Visser M, Schmand BA, de Haan RJ. The cognitive profile of ALS: a systematic review and meta-analysis update. J Neurol Neurosurg Psychiatry. 2016; 87(6): 611–9. pmid:26283685
  19. 19. Crespi C, Cerami C, Dodich A, Canessa N, Arpone M, Iannaccone S, et al. Microstructural white matter correlates of emotion recognition impairment in Amyotrophic Lateral Sclerosis. Cortex. 2014; 53: 1–8. pmid:24534360
  20. 20. Zimmerman EK, Zachary Simmons MD, Barrett AM. (2007). Emotional perception deficits in amyotrophic lateral sclerosis. Cogn Behav Neurol. 2007; 20(2): 79. pmid:17558250
  21. 21. Lulé D, Kurt A, Jürgens R, Kassubek J, Diekmann V, Kraft E, et al. Emotional responding in amyotrophic lateral sclerosis. J Neurol. 2005; 252(12): 1517–1524. pmid:15977000
  22. 22. Palmieri A, Naccarato M, Abrahams S, Bonato M, D’Ascenzo C, Balestreri S, et al. Right hemisphere dysfunction and emotional processing in ALS: an fMRI study. J Neurol. 2010; 257(12): 1970–1978. pmid:20593194
  23. 23. Papps B, Abrahams S, Wicks P, Leigh PN, Goldstein LH. Changes in memory for emotional material in amyotrophic lateral sclerosis (ALS). Neuropsychologia. 2005; 43(8): 1107–1114. pmid:15817168
  24. 24. Girardi A, MacPherson SE, Abrahams S. Deficits in emotional and social cognition in amyotrophic lateral sclerosis. Neuropsychology. 2011; 25(1): 53. pmid:20919762
  25. 25. Meier SL, Charleston AJ, Tippett LJ. Cognitive and behavioural deficits associated with the orbitomedial prefrontal cortex in amyotrophic lateral sclerosis. Brain. 2010; 133(11): 3444–3457. pmid:20889583
  26. 26. Cavallo M, Adenzato M, MacPherson SE, Karwig G, Enrici I, Abrahams S. Evidence of social understanding impairment in patients with amyotrophic lateral sclerosis. PLoS One. 2011; 6(10): e25948–e25948. pmid:21998727
  27. 27. Cerami C, Dodich A, Canessa N, Crespi C, Iannaccone S, Corbo M, et al. Emotional empathy in amyotrophic lateral sclerosis: a behavioural and voxel-based morphometry study. Amyotroph Lateral Scler Frontotemporal Degener. 2014; 15(1–2): 21–29. pmid:23586919
  28. 28. Gibbons ZC, Snowden JS, Thompson JC, Happe F, Richardson A, Neary D. Inferring thought and action in motor neurone disease. Neuropsychologia. 2007; 45(6): 1196–1207. pmid:17118410
  29. 29. Shamay-Tsoory SG, Aharon-Peretz J, Perry D. Two systems for empathy: a double dissociation between emotional and cognitive empathy in inferior frontal gyrus versus ventromedial prefrontal lesions. Brain. 2009; 132(3): 617–627.
  30. 30. Shamay-Tsoory SG. The neural bases for empathy. Neuroscientist. 2011; 17(1): 18–24. pmid:21071616
  31. 31. Raz G, Jacob Y, Gonen T, Winetraub Y, Flash T, Soreq E, Hendler T. Cry for her or cry with her: context-dependent dissociation of two modes of cinematic empathy reflected in network cohesion dynamics. Soc Cogn Affect Neurosci. 2014; 9(1): 30–38. pmid:23615766
  32. 32. Piguet O, Hornberger M, Mioshi E, Hodges JR. Behavioural-variant frontotemporal dementia: diagnosis, clinical staging, and management. Lancet Neurol. 2011; 10(2): 162–172. pmid:21147039
  33. 33. Seeley WW, Crawford R, Rascovsky K, Kramer JH, Weiner M, Miller BL, Gorno-Tempini ML. Frontal paralimbic network atrophy in very mild behavioral variant frontotemporal dementia. Arch Neurol. 2008; 65(2): 249–255. pmid:18268196
  34. 34. Adenzato M, Cavallo M, Enrici I. Theory of mind ability in the behavioural variant of frontotemporal dementia: an analysis of the neural, cognitive, and social levels. Neuropsychologia. 2010; 48(1): 2–12. pmid:19666039
  35. 35. Cerami C, Cappa SF. The behavioral variant of frontotemporal dementia: linking neuropathology to social cognition. Neurol Sci. 2013; 34(8): 1267–1274. pmid:23377232
  36. 36. Cerami C, Dodich A, Canessa N, Crespi C, Marcone A, Cortese F, et al. Neural correlates of empathic impairment in the behavioral variant of frontotemporal dementia. Alzheimers Dement. 2014; 10(6): 827–834. pmid:24589435
  37. 37. Kipps CM, Nestor PJ, Acosta-Cabronero J, Arnold R, Hodges JR. Understanding social dysfunction in the behavioural variant of frontotemporal dementia: the role of emotion and sarcasm processing. Brain. 2009; 132(3): 592–603.
  38. 38. Kumfor F, Piguet O. Disturbance of emotion processing in frontotemporal dementia: a synthesis of cognitive and neuroimaging findings. Neuropsychol Review. 2012; 22(3): 280–297.
  39. 39. Caminiti SP, Canessa N, Cerami C, Dodich A, Crespi C, Iannaccone S, et al. Affective mentalizing and brain activity at rest in the behavioral variant of frontotemporal dementia. NeuroImage Clin. 2015; 9: 484–497. pmid:26594631
  40. 40. Brooks BR, Miller RG, Swash M, Munsat TL. El Escorial revisited: revised criteria for the diagnosis of amyotrophic lateral sclerosis. Amyotroph Lateral Scler Other Motor Neuron Disord 2000; 1(5): 293–299. pmid:11464847
  41. 41. Cedarbaum JM, Stambler N, Malta E, Fuller C, Hilt D, Thurmond B, et al. The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function. J Neurol Sci. 1999; 169(1): 13–21.
  42. 42. Strong MJ, Grace GM, Freedman M, Lomen-Hoerth C, Woolley S, Goldstein LH, et al. Consensus criteria for the diagnosis of frontotemporal cognitive and behavioural syndromes in amyotrophic lateral sclerosis. Amyotroph Lateral Sc. 2009; 10(3): 131–146.
  43. 43. Dodich A, Cerami C, Canessa N, Crespi C, Iannaccone S, Marcone A, et al. A novel task assessing intention and emotion attribution: Italian standardization and normative data of the Story-based Empathy Task. Neurol Sci. 2015; 36(10): 1907–1912. pmid:26072203
  44. 44. Brunet E, Sarfati Y, Hardy-Baylé MC, Decety J. A PET investigation of the attribution of intentions with a nonverbal task. Neuroimage. 2000; 11(2): 157–166. pmid:10679187
  45. 45. Sarfati Y, Hardy-Baylé MC, Besche C, Widlöcher D. Attribution of intentions to others in people with schizophrenia: a non-verbal exploration with comic strips. Schizophr Res. 1997; 25(3): 199–209. pmid:9264175
  46. 46. Völlm BA, Taylor AN, Richardson P, Corcoran R, Stirling J, McKie S, et al. Neuronal correlates of theory of mind and empathy: a functional magnetic resonance imaging study in a nonverbal task. Neuroimage. 2006; 29(1): 90–98. pmid:16122944
  47. 47. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006; 31(4): 1487–1505. pmid:16624579
  48. 48. Smith SM, Nichols TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage. 2009; 44(1): 83–98. pmid:18501637
  49. 49. Hua K, Zhang J, Wakana S, Jiang H, Li X, Reich DS, et al. Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage. 2008; 39(1): 336–347. pmid:17931890
  50. 50. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B, 1995; 289–300.
  51. 51. Watermeyer TJ, Brown RG, Sidle KC, Oliver DJ, Allen C, Karlsson J, et al. Executive dysfunction predicts social cognition impairment in amyotrophic lateral sclerosis. J Neurol. 2015; 1–10.
  52. 52. Canu E, Agosta F, Riva N, Sala S, Prelle A, Caputo D, et al. The topography of brain microstructural damage in amyotrophic lateral sclerosis assessed using diffusion tensor MR imaging. AJNR Am J Neuroradiol. 2011; 32(7): 1307–1314. pmid:21680655
  53. 53. Filippini N, Douaud G, Mackay CE, Knight S, Talbot K, Turner MR. Corpus callosum involvement is a consistent feature of amyotrophic lateral sclerosis. Neurology. 2010; 75(18): 1645–1652. pmid:21041787
  54. 54. Kim MJ, Loucks RA, Palmer AL, Brown AC, Solomon KM, Marchante AN, Whalen PJ. The structural and functional connectivity of the amygdala: from normal emotion to pathological anxiety. Behav Brain Res. 2011; 223(2): 403–410. pmid:21536077
  55. 55. Von Der Heide RJ, Skipper LM, Klobusicky E, Olson IR. Dissecting the uncinate fasciculus: disorders, controversies and a hypothesis. Brain. 2013; 136(6): 1692–1707.
  56. 56. Gallese V. Embodied simulation: From neurons to phenomenal experience. Phenomenol Cogn Sci. 2005; 4(1): 23–48.
  57. 57. Tamietto M, Adenzato M, Geminiani G, de Gelder B. Fast recognition of social emotions takes the whole brain: interhemispheric cooperation in the absence of cerebral asymmetry. Neuropsychologia. 2007; 45(4): 836–843. pmid:16996092
  58. 58. Parkinson C, Wheatley T. Relating anatomical and social connectivity: white matter microstructure predicts emotional empathy. Cereb Cortex. 2014; 24(3): 614–625. pmid:23162046
  59. 59. Iacoboni M, Dapretto M. The mirror neuron system and the consequences of its dysfunction. Nat Rev Neurosci. 2006; 7(12): 942–951. pmid:17115076
  60. 60. ] Bridgman MW, Brown WS, Spezio ML, Leonard MK, Adolphs R, Paul LK. Facial emotion recognition in agenesis of the corpus callosum. J Neurodev Disord. 2014; 6(1): 1–14.
  61. 61. Fujino J, Takahashi H, Miyata J, Sugihara G, Kubota M, Sasamoto A, et al. Impaired empathic abilities and reduced white matter integrity in schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry. 2014; 48: 117–123. pmid:24099786
  62. 62. Hinkley LB, Marco EJ, Findlay AM, Honma S, Jeremy RJ, Strominger Z, et al. The role of corpus callosum development in functional connectivity and cognitive processing. PLoS One. 2012; 7(8): e39804. pmid:22870191
  63. 63. Miyata J, Yamada M, Namiki C, Hirao K, Saze T, Fujiwara H, et al. Reduced white matter integrity as a neural correlate of social cognition deficits in schizophrenia. Schizophr Res. 2010; 119(1): 232–239.
  64. 64. Paul LK, Brown WS, Adolphs R, Tyszka JM, Richards LJ, Mukherjee P, Sherr EH. Agenesis of the corpus callosum: genetic, developmental and functional aspects of connectivity. Nat Rev Neurosci. 2007; 8(4): 287–299. pmid:17375041
  65. 65. Paul LK, Corsello C, Kennedy DP, Adolphs R. Agenesis of the corpus callosum and autism: a comprehensive comparison. Brain. 2014; awu070.
  66. 66. Matsuo K, Mizuno T, Yamada K, Akazawa K, Kasai T, Kondo M, et al. Cerebral white matter damage in frontotemporal dementia assessed by diffusion tensor tractography. Neuroradiology. 2008; 50(7); 605–611. pmid:18379765
  67. 67. Whitwell JL, Avula R, Senjem ML, Kantarci K, Weigand SD, Samikoglu A, et al. Gray and white matter water diffusion in the syndromic variants of frontotemporal dementia. Neurology. 2010; 74(16): 1279–1287. pmid:20404309
  68. 68. Zhang Y, Schuff N, Du AT, Rosen HJ, Kramer JH, Gorno-Tempini ML, et al. White matter damage in frontotemporal dementia and Alzheimer's disease measured by diffusion MRI. Brain. 2009; 132(9): 2579–2592.
  69. 69. Eisen A, Lemon R, Kiernan MC, Hornberger M, Turner MR. Does dysfunction of the mirror neuron system contribute to symptoms in amyotrophic lateral sclerosis?. Clin Neurophysiol. 2015; 126(7): 1288–1294. pmid:25727900
  70. 70. Craig AD. How do you feel—now? The anterior insula and human awareness. Nat Rev Neurosci. 2009; 10(1).
  71. 71. Craig AD. Interoception and emotion: a neuroanatomical perspective. Handbook of emotions. 2008; 3:272–288.
  72. 72. Meyer ML, Masten CL, Ma Y, Wang C, Shi Z, Eisenberger NI, Han S. Empathy for the social suffering of friends and strangers recruits distinct patterns of brain activation. Soc Cogn Affect Neurosci. 2012; nss019.