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Segmentation of medial temporal subregions reveals early right-sided involvement in semantic variant PPA

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Alzheimer's Research & Therapy201911:41

https://doi.org/10.1186/s13195-019-0489-9

  • Received: 3 January 2019
  • Accepted: 2 April 2019
  • Published:

Abstract

Background

Semantic variant of primary progressive aphasia (svPPA) is a subtype of frontotemporal dementia characterized by asymmetric temporal atrophy.

Methods

We investigated the pattern of medial temporal lobe atrophy in 24 svPPA patients compared to 72 controls using novel approaches to segment the hippocampal and amygdalar subregions on MRIs. Based on semantic knowledge scores, we split the svPPA group into 3 subgroups of early, middle and late disease stage.

Results

Early stage: all left amygdalar and hippocampal subregions (except the tail) were affected in svPPA (21–35% smaller than controls), together with the following amygdalar nuclei in the right hemisphere: lateral, accessory basal and superficial (15–23%). On the right, only the temporal pole was affected among the cortical regions. Middle stage: the left hippocampal tail became affected (28%), together with the other amygdalar nuclei (22–26%), and CA4 (15%) on the right, with orbitofrontal cortex and subcortical structures involvement on the left, and more posterior temporal lobe on the right. Late stage: the remaining right hippocampal regions (except the tail) (19–24%) became affected, with more posterior left cortical and right extra-temporal anterior cortical involvement.

Conclusions

With advanced subregions segmentation, it is possible to detect early involvement of the right medial temporal lobe in svPPA that is not detectable by measuring the amygdala or hippocampus as a whole.

Keywords

  • Semantic variant PPA
  • Magnetic resonance imaging
  • Medial temporal subregions

Introduction

Semantic variant of primary progressive aphasia (svPPA) is a subtype of frontotemporal dementia (FTD), characterized clinically by anomia and impaired single-word comprehension. It is associated with a characteristic pattern of asymmetrical antero-inferior temporal lobe atrophy [13]. Previous studies of svPPA have shown early left medial temporal lobe involvement, with both hippocampal and amygdalar atrophy [46]. However, these studies have investigated the whole hippocampus or amygdala and no previous studies have looked at the subregions of the medial temporal lobe. In this study, we therefore aimed to investigate the pattern of atrophy of the subregions of the hippocampus and the amygdala in svPPA, focusing on the involvement at different stages in order to understand the areas involved early in the disease process.

Methods

We reviewed the UCL Dementia Research Centre FTD MRI database to identify patients with a diagnosis of svPPA [7] and a usable 3 T T1-weighted magnetic resonance (MR) scan. Twenty-four patients were identified, all with left-temporal predominant disease. Seventy-two cognitively normal subjects with a usable volumetric 3 T T1-weighted MRI were identified as controls. The study was approved by the local ethics committee, and written informed consent was obtained from all participants. The study was conducted in accordance with the Helsinki Declaration of 1975.

Based on their scores on a test of semantic knowledge (the British Picture Vocabulary Scale, BPVS, a word-picture matching task) [8], we split the svPPA patients into three equal subgroups (n = 8 per group) of early (BPVS > 110/150), middle (BPVS = 55–110/150) and late disease stage (BPVS < 55/150). Patients were negative for mutations in all FTD-related genes. Two patients received post-mortem confirmation of the underlying neuropathology, both TDP-43 type C.

All patients underwent a detailed neuropsychological examination including tests of fluid intelligence (WASI Matrices), single-word comprehension (WASI Vocabulary), naming (Graded Naming Test), reading (National Adult Reading Test), verbal memory (Recognition Memory Test for Words), visual memory (Recognition Memory Test for Faces), short-term memory (forwards digit span), working memory (backwards digit span), calculation (Graded Difficulty Calculation Test), visuoperceptual function (Visual Object and Space Perception battery Object Decision subtest) and executive function (inhibition—D-KEFS Color-Word Ink Naming Test; abstract reasoning—WASI Similarities). A percentile score based on standard norms was generated for each patient, with a mean percentile score created for the early, middle and late stage groups. Assessment of behavioural symptoms was performed using the revised version of the Cambridge Behavioural Inventory (CBI-R) [9]: six subscores were used (difficulties with self-care, abnormal sleep, hallucinations/delusions, disinhibition, abnormal eating behaviour, obsessive-compulsive behaviour, apathy and loss of empathy) with a percentage of the total possible subscore generated for every patient; for each stage, a mean percentage score was created. We report the cognitive and behavioural profiles at each stage for illustrative purposes (Fig. 1 and Additional file 1: Table S1).
Fig. 1
Fig. 1

Pattern of atrophy in amygdalar subnuclei, hippocampal subfields, cortical regions and subcortical structures across early, middle and late stages of svPPA. Colour bar denotes the percent difference in volume from controls that remained significant after correction for multiple comparisons. For illustrative purposes, we have included the changes in cognition [mean percentile scores] and behavioural changes [mean percentage score in each Cambridge Behavioural Inventory subscore] that occur at these stages. The length of the segment indicates the severity of the profile. Specifically, for the cognitive performance, the smaller the segment, the worse the performance, whilst for the behavioural symptoms, the bigger the segments, the worse the symptoms

T1-weighted MRIs were acquired using a 3-T scanner, either a Trio (Siemens, Erlangen, Germany, TR = 2200 ms, TI = 900 ms, TE = 2.9 ms, acquisition matrix = 256 × 256, spatial resolution = 1.1 mm) or a Prisma (Siemens, Erlangen, Germany, TR = 2000 ms, TI = 850 ms, TE = 2.93 ms, acquisition matrix = 256 × 256, spatial resolution = 1.1 mm). Individuals with moderate to severe vascular disease or space-occupying lesions were excluded.

Volumetric MRI scans were first bias field corrected and whole-brain parcellated using the geodesic information flow (GIF) algorithm [10], which is based on atlas propagation and label fusion. The hippocampal subfields and amygdalar subregions were subsequently segmented using a customized version of the module available in FreeSurfer 6.0 [11, 12], to adapt the output of GIF to the FreeSurfer format. For the hippocampal subfields, we focused on seven areas: CA1, CA2/CA3, CA4, dentate gyrus, subiculum, presubiculum and the tail. We excluded from the analysis the hippocampus-amygdala transition area, the parasubiculum, the molecular layer of the hippocampus, the fimbria and the hippocampal fissure, as they were too small, or not reliably delineated on T1-weighted images. For the amygdalar subnuclei, we focused the analysis on five regions, by combining the smallest subnuclei, based on an anatomical subdivision [13]: lateral nucleus, basal and paralaminar nucleus, accessory basal nucleus, cortico-amygdaloid transition area and the superficial nuclei (central nucleus, cortical nucleus, medial nucleus, anterior amygdaloid area).

For comparison with the medial temporal subregions, we extracted volumes of the following cortical regions from GIF: temporal (medial, lateral, supratemporal, temporal pole), frontal (orbitofrontal, prefrontal), parietal, occipital, insular and cingulate (anterior and posterior). We also extracted volumes of subcortical structures for the pallidum, putamen, caudate, nucleus accumbens and thalamus.

Left and right volumes were corrected for total intracranial volume (TIV), computed with SPM12 v6470 (Statistical Parametric Mapping, Wellcome Trust Centre for Neuroimaging, London, UK) running under Matlab R2014b (Math Works, Natick, MA, USA) [14]. All segmentations were visually checked for quality.

Statistical analyses were performed on brain volumes (as a percentage of TIV) in STATA v14 (Stata-Corp, College Station, TX), between control and patients (early, middle and late stage groups), using a linear regression test adjusting for scanner type, TIV, gender and age. The results were corrected for multiple comparisons (Bonferroni correction): p < 0.006 for amygdalar subnuclei and subcortical structures, p < 0.005 for hippocampal subfields and p < 0.0035 for cortical regions.

Results

No significant age difference was seen between any of the svPPA groups and controls [Early: 66.9 (5.5) years, Middle: 64.5 (9.5), Late: 64.2 (5.5); Controls: 61.0 (12.1)], p = 0.112, t test. However, there was a significant difference in gender distribution across stages [Early: 88% male, Middle: 63% male, Late: 25% male; Controls: 40% male], p = 0.032, Chi-square test.

Amygdalar subnuclei, hippocampal subfields, cortical regions, subcortical structures, neuropsychology performance and behavioural symptoms at each stage are shown in Fig. 1.

Early stage

All the left amygdalar and hippocampal subregions (except for the tail) were affected (24–35% and 21–27% smaller than controls, p < 0.0005) at this stage, together with the right lateral, accessory basal and superficial nuclei of the amygdala (15–23%, p < 0.004) (Table 1).
Table 1

Volumetry of amygdalar subnuclei, hippocampal subfields, cortical regions and subcortical structures

   

Controls

Early

Middle

  

Controls

Early

Middle

Left

Right

Mean

SD

%

p-value

%

p-value

%

p-value

Mean

SD

%

p-value

%

p-value

%

p-value

Amygdalar Subnuclei

 Lateral nucleus

  Controls

0.045

0.005

      

0.047

0.004

      

  Early

0.033

0.010

27

< 0.0005

    

0.040

0.006

15

0.003

    

  Middle

0.026

0.003

43

< 0.0005

23

< 0.0005

  

0.035

0.005

25

< 0.0005

12

0.005

  

  Late

0.025

0.003

44

< 0.0005

24

< 0.0005

2

0.723

0.030

0.005

36

< 0.0005

25

< 0.0005

14

0.017

 Basal and paralaminar nucleus

  Controls

0.033

0.004

      

0.034

0.003

      

  Early

0.024

0.006

29

< 0.0005

    

0.029

0.006

15

0.012

    

  Middle

0.018

0.003

46

< 0.0005

24

< 0.0005

  

0.026

0.004

22

< 0.0005

8

0.092

  

  Late

0.017

0.002

48

< 0.0005

27

< 0.0005

4

0.483

0.021

0.003

39

< 0.0005

29

< 0.0005

22

< 0.0005

 Accessory basal nucleus

  Controls

0.018

0.002

      

0.018

0.002

      

  Early

0.012

0.004

32

< 0.0005

    

0.015

0.004

21

< 0.0005

    

  Middle

0.010

0.002

46

< 0.0005

20

0.002

  

0.014

0.002

24

< 0.0005

4

0.373

  

  Late

0.009

0.001

49

< 0.0005

25

< 0.0005

6

0.482

0.011

0.002

42

< 0.0005

27

< 0.0005

24

0.002

 Cortico-amygdaloid transition area

  Controls

0.012

0.002

      

0.012

0.001

      

  Early

0.009

0.002

24

< 0.0005

    

0.011

0.003

12

0.157

    

  Middle

0.007

0.001

44

< 0.0005

27

< 0.0005

  

0.009

0.002

24

< 0.0005

14

0.025

  

  Late

0.006

0.001

48

< 0.0005

32

< 0.0005

7

0.339

0.008

0.002

36

< 0.0005

28

< 0.0005

16

0.049

 Superficial nuclei (Ce, Co, Me, AAA)

  Controls

0.011

0.002

      

0.012

0.002

      

  Early

0.007

0.002

35

< 0.0005

    

0.009

0.002

23

0.004

    

  Middle

0.006

0.001

47

< 0.0005

18

0.005

  

0.009

0.001

26

< 0.0005

4

0.341

  

  Late

0.005

0.001

51

< 0.0005

25

< 0.0005

9

0.275

0.007

0.002

41

< 0.0005

24

0.002

21

0.024

Hippocampal Subfields

 CA1

  Controls

0.044

0.005

      

0.047

0.006

      

  Early

0.035

0.005

22

< 0.0005

    

0.045

0.008

5

0.995

    

  Middle

0.031

0.007

31

< 0.0005

11

0.020

  

0.043

0.007

8

0.138

3

0.267

  

  Late

0.029

0.004

36

< 0.0005

18

0.001

7

0.268

0.036

0.006

24

< 0.0005

19

< 0.0005

17

0.003

 CA2/CA3

  Controls

0.016

0.002

      

0.017

0.002

      

  Early

0.012

0.002

24

< 0.0005

    

0.016

0.004

6

0.931

    

  Middle

0.011

0.002

27

< 0.0005

3

0.460

  

0.015

0.003

12

0.064

7

0.184

  

  Late

0.012

0.002

26

< 0.0005

2

0.518

−1

0.945

0.013

0.002

24

< 0.0005

19

0.002

13

0.054

 CA4

  Controls

0.018

0.002

      

0.019

0.002

      

  Early

0.013

0.002

27

< 0.0005

    

0.017

0.004

10

0.281

    

  Middle

0.013

0.001

27

< 0.0005

1

0.342

  

0.016

0.002

15

0.003

5

0.156

  

  Late

0.012

0.001

34

< 0.0005

9

0.007

9

0.066

0.015

0.002

21

< 0.0005

13

0.004

8

0.111

 Dentate gyrus

  Controls

0.021

0.002

      

0.021

0.002

      

  Early

0.016

0.002

25

< 0.0005

    

0.020

0.005

7

0.759

    

  Middle

0.015

0.002

27

< 0.0005

4

0.183

  

0.019

0.003

13

0.021

6

0.132

  

  Late

0.014

0.002

32

< 0.0005

10

0.011

6

0.185

0.017

0.003

19

< 0.0005

14

0.003

8

0.117

 Subiculum

  Controls

0.028

0.003

      

0.029

0.003

      

  Early

0.022

0.002

21

< 0.0005

    

0.028

0.006

1

0.425

    

  Middle

0.020

0.003

28

< 0.0005

10

0.048

  

0.026

0.005

8

0.116

8

0.074

  

  Late

0.020

0.004

31

< 0.0005

13

0.005

4

0.338

0.022

0.005

23

< 0.0005

23

< 0.0005

16

0.004

 Presubiculum

  Controls

0.023

0.003

      

0.022

0.003

      

  Early

0.017

0.002

27

< 0.0005

    

0.023

0.006

−2

0.173

    

  Middle

0.016

0.002

30

< 0.0005

5

0.362

  

0.021

0.007

5

0.942

6

0.267

  

  Late

0.016

0.003

33

< 0.0005

8

0.045

3

0.245

0.018

0.005

19

0.001

20

0.001

15

0.015

 Hippocampal tail

  Controls

0.041

0.005

      

0.041

0.005

      

  Early

0.034

0.006

18

0.019

    

0.043

0.010

−4

0.055

    

  Middle

0.030

0.005

28

< 0.0005

12

0.026

  

0.042

0.010

−2

0.371

2

0.41

  

  Late

0.029

0.006

29

< 0.0005

13

0.009

2

0.624

0.037

0.008

8

0.084

12

0.008

10

0.054

Cortical Regions

 Orbitofrontal

  Controls

0.697

0.047

      

0.716

0.048

      

  Early

0.682

0.045

2

0.934

    

0.727

0.057

−2

0.158

    

  Middle

0.629

0.089

10

0.001

8

0.015

  

0.716

0.046

0

0.806

1

0.362

  

  Late

0.637

0.063

9

0.009

7

0.062

−1

0.612

0.697

0.078

3

0.647

4

0.166

3

0.604

 Prefrontal cortex

  Controls

4.216

0.230

      

4.322

0.224

      

  Early

4.087

0.337

3

0.691

    

4.299

0.379

1

0.545

    

  Middle

4.045

0.529

4

0.112

1

0.373

  

4.380

0.369

−1

0.506

−2

0.977

  

  Late

3.806

0.250

10

0.002

7

0.047

6

0.245

4.119

0.269

5

0.201

4

0.168

6

0.153

 Anterior cingulate

  Controls

0.382

0.039

      

0.283

0.042

      

  Early

0.315

0.041

18

0.001

    

0.289

0.046

−2

0.339

    

  Middle

0.300

0.068

22

< 0.0005

5

0.311

  

0.318

0.069

−13

0.008

−10

0.204

  

  Late

0.255

0.026

33

< 0.0005

19

0.002

15

0.023

0.288

0.058

−2

0.968

0

0.457

9

0.047

 Posterior cingulate

  Controls

0.359

0.038

      

0.343

0.035

      

  Early

0.350

0.020

3

0.609

    

0.368

0.019

−7

0.009

    

  Middle

0.332

0.025

7

0.065

5

0.320

  

0.365

0.028

−6

0.022

1

0.747

  

  Late

0.337

0.028

6

0.169

4

0.535

−1

0.728

0.361

0.047

−5

0.150

2

0.348

1

0.523

 Parietal

  Controls

3.224

0.211

      

3.186

0.229

      

  Early

3.143

0.229

3

0.538

    

3.216

0.248

−1

0.049

    

  Middle

3.147

0.249

2

0.709

0

0.450

  

3.272

0.200

−3

0.053

−2

0.944

  

  Late

2.993

0.234

7

0.003

5

0.008

5

0.046

3.142

0.213

1

0.793

2

0.096

4

0.105

 Occipital

  Controls

2.473

0.207

      

2.564

0.205

      

  Early

2.393

0.227

3

0.835

    

2.538

0.195

1

0.575

    

  Middle

2.395

0.155

3

0.552

0

0.776

  

2.552

0.175

0

0.697

−1

0.887

  

  Late

2.432

0.148

2

0.733

−2

0.926

−2

0.853

2.572

0.147

0

0.796

−1

0.817

−1

0.924

 Insula

  Controls

0.370

0.035

      

0.381

0.039

      

  Early

0.281

0.032

24

< 0.0005

    

0.343

0.049

10

0.110

    

  Middle

0.260

0.036

30

< 0.0005

7

0.064

  

0.337

0.038

12

0.007

2

0.425

  

  Late

0.229

0.021

38

< 0.0005

18

< 0.0005

12

0.013

0.267

0.039

30

< 0.0005

22

< 0.0005

21

< 0.0005

 Medial temporal

  Controls

1.012

0.062

      

1.041

0.067

      

  Early

0.785

0.057

22

< 0.0005

    

0.981

0.070

6

0.076

    

  Middle

0.730

0.056

28

< 0.0005

7

0.042

  

0.915

0.070

12

< 0.0005

7

0.044

  

  Late

0.743

0.058

27

< 0.0005

5

0.088

−2

0.787

0.791

0.074

24

< 0.0005

19

< 0.0005

14

< 0.0005

 Lateral temporal

  Controls

2.304

0.153

      

2.345

0.143

      

  Early

1.652

0.201

28

< 0.0005

    

2.231

0.134

5

0.133

    

  Middle

1.554

0.150

33

< 0.0005

6

0.084

  

2.137

0.099

9

< 0.0005

4

0.105

  

  Late

1.384

0.159

40

< 0.0005

16

< 0.0005

11

0.026

1.864

0.217

21

< 0.0005

16

< 0.0005

13

< 0.0005

 Temporal pole

  Controls

0.488

0.056

      

0.477

0.055

      

  Early

0.261

0.066

47

< 0.0005

    

0.413

0.071

13

0.006

    

  Middle

0.231

0.035

53

< 0.0005

12

0.187

  

0.352

0.049

26

< 0.0005

15

0.019

  

  Late

0.228

0.029

53

< 0.0005

13

0.324

1

0.766

0.287

0.038

40

< 0.0005

30

< 0.0005

18

0.048

 Supratemporal

  Controls

0.430

0.050

      

0.369

0.039

      

  Early

0.348

0.037

19

< 0.0005

    

0.357

0.045

3

0.910

    

  Middle

0.336

0.046

22

< 0.0005

4

0.359

  

0.368

0.040

0

0.718

−3

0.855

  

  Late

0.301

0.056

30

<0.0005

14

0.017

10

0.122

0.322

0.054

13

0.004

10

0.028

12

0.016

Subcortical Structures

 Nucleus accumbens

  Controls

0.040

0.003

      

0.038

0.003

      

  Early

0.035

0.003

13

<0.0005

    

0.035

0.003

9

0.048

    

  Middle

0.034

0.005

15

<0.0005

3

0.235

  

0.036

0.004

5

0.155

−4

0.638

  

  Late

0.030

0.003

24

<0.0005

13

0.001

10

0.019

0.032

0.004

15

<0.0005

7

0.026

11

0.007

 Caudate

  Controls

0.237

0.026

      

0.248

0.024

      

  Early

0.221

0.020

7

0.508

    

0.235

0.026

5

0.598

    

  Middle

0.222

0.026

6

0.350

0

0.851

  

0.237

0.024

4

0.507

−1

0.929

  

  Late

0.207

0.030

12

0.001

6

0.037

7

0.053

0.217

0.036

12

0.001

8

0.044

8

0.050

 Pallidum

  Controls

0.129

0.014

      

0.130

0.013

      

  Early

0.114

0.007

12

0.010

    

0.119

0.007

8

0.123

    

  Middle

0.113

0.008

12

<0.0005

1

0.160

  

0.119

0.008

8

0.004

0

0.303

  

  Late

0.104

0.009

19

<0.0005

9

0.016

8

0.270

0.111

0.011

14

<0.0005

6

0.054

7

0.336

 Putamen

  Controls

0.307

0.031

      

0.305

0.031

      

  Early

0.268

0.018

13

0.011

    

0.289

0.016

5

0.981

    

  Middle

0.255

0.023

17

<0.0005

5

0.044

  

0.277

0.023

9

0.019

4

0.081

  

  Late

0.237

0.018

23

<0.0005

11

0.001

7

0.144

0.261

0.022

14

<0.0005

10

0.002

6

0.142

 Thalamus

  Controls

0.400

0.035

      

0.392

0.039

      

  Early

0.357

0.024

11

0.024

    

0.380

0.032

3

0.279

    

  Middle

0.362

0.029

9

0.008

−2

0.791

  

0.387

0.036

1

0.258

−2

0.992

  

  Late

0.364

0.027

9

<0.0005

−2

0.169

−1

0.255

0.388

0.027

1

0.226

−2

0.093

0

0.086

Values denote mean and standard deviation (SD) volumes as the percentage of the total intracranial volume (TIV) or difference (%). p values denote significance on linear regression test. Bold represents a significant difference between the groups after correcting for multiple comparisons

Outside of the medial temporal lobe, on the left, all the temporal cortical regions (19–47%, p < 0.0005) were affected as well as the anterior cingulate (18%, p = 0.001) and insula (24%, p < 0.0005). The left nucleus accumbens was the only other subcortical structure affected (13%, p < 0.0005). Apart from the affected amygdalar subnuclei, the only other right hemisphere structure affected at this stage was the temporal pole (13%, p = 0.006).

Cognitively, patients showed severely impaired naming already, with relatively preserved working memory, abstract reasoning and fluid intelligence. Behavioural symptoms were mild and mainly related to abnormal eating behaviour, apathy and abnormal sleep.

Middle stage

At this stage, the left hippocampal tail became affected (28%, p < 0.0005), together with the other right amygdalar nuclei (22–26%, p < 0.0005) and the right CA4 region of the hippocampus (15%, p = 0.003).

Cortically, the left orbitofrontal lobe was affected at this stage along with more posterior temporal structures on the right: lateral and medial temporal cortices (9–12%, p < 0.0005). Subcortically, the left pallidum and putamen were affected (12–17%, p < 0.0005) and the right pallidum (8%).

Cognitively, single-word comprehension and reading became increasingly impaired, but working memory, short-term memory and abstract reasoning remained relatively intact. Behavioural symptoms increased with the presence of obsessive-compulsive behaviour and loss of empathy as well as abnormal eating behaviour, apathy and disinhibition.

Late stage

In the late stage, the remaining right hippocampal regions (except the tail) (19–24%, p < 0.001) became affected.

Cortically, spread to the left prefrontal and parietal cortices was seen whilst on the right, the insula (30%) and supratemporal cortex (13%, p < 0.004) were affected. Subcortically, the left caudate, thalamus and right nucleus accumbens, caudate and putamen were affected (12–15%).

At this stage, all cognitive domains were severely impaired except for short-term and working memory, abstract reasoning and fluid intelligence. Severe behavioural symptoms were seen.

Discussion

Using advanced subregional segmentation, we were able to detect early involvement in the right hemisphere in svPPA, with progression of atrophy through the medial temporal lobes as the disease moves from early to middle to late stage.

Extensive medial temporal atrophy is seen on the left in most amygdalar and hippocampal subregions at the earliest stage of svPPA, co-incidental with the involvement of all of the temporal cortices on the left. This is consistent with previous studies showing that even at first clinical presentation, significant left temporal lobe atrophy is present [1, 15].

Previous studies have not shown early involvement of the right medial temporal structures. In this study, the earliest subnuclei affected on the right were the accessory basal, lateral and superficial nuclei of the amygdala. These subnuclei are interconnected and receive input from the temporal pole and the hippocampus (also affected on the right in the early stage) as well as other parts of the temporal and frontal cortices and the nucleus accumbens [13, 16]. The ability to use advanced subregional segmentation techniques in this study allows early detection of right medial temporal atrophy.

The cognitive and behavioural correlates of the individual right amygdalar subnuclei are poorly studied, but prior studies of the whole amygdala implicate the right side as being important in the processing of emotional information [17, 18]. In our study, loss of empathy is mildly affected at the earliest stage (Fig. 1): this is likely to represent an impairment of self-knowledge, a process that requires the linking of emotions with semantics, and has previously been shown to be associated with right temporal lobe atrophy including the amygdala [19]. The particular amygdalar subnuclei affected early are part of the limbic network and therefore likely to be intrinsically involved in emotion processing [16].

Of all the medial temporal subregions, the hippocampal tail is preserved until the later stages of svPPA. This is in line with previous studies, where the posterior temporal lobe is spared and an antero-posterior gradient is present [20, 21]. Indeed, svPPA patients typically show intact episodic memory and spatial navigation, functions typically linked to the hippocampal tail. Consistent with the theory of svPPA as a network-opathy [22], the first hippocampal region to become affected on the right is CA4, an area highly connected to the temporal cortex and amygdala [23].

Limitations of the study include using cross-sectional data with staging of the disease by impairment on a task of semantic knowledge and the small number of svPPA cases. Further studies would benefit from the analysis of longitudinal data from a larger sample to see whether the same pattern is seen. Despite the gold standard still being manual segmentation of dedicated MRIs or on brain tissue post-mortem, these automated methods included in this study have been previously validated and proven reliable to delineate the subregions on T1-MRI (Dice coefficients > 0.86; ICC 0.88–0.93) [1012, 24, 25]. Moreover, in this study, we carefully excluded small subregions and combined together groups of nuclei to improve the anatomical validity. Automated segmentations will play a key role in the future, as manual segmentations are likely to be unfeasible for large cohorts of patients.

Declarations

Acknowledgements

Not applicable.

Funding

The Dementia Research Centre is supported by Alzheimer’s Research UK, Brain Research Trust and The Wolfson Foundation. This work was supported by the NIHR Queen Square Dementia Biomedical Research Unit and the NIHR UCL/H Biomedical Research Centre, the MRC UK GENFI grant (MR/M023664/1) and the Alzheimer’s Society (AS-PG-16-007). JDR is supported by an MRC Clinician Scientist Fellowship (MR/M008525/1) and has received funding from the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH). JDW was supported by a Wellcome Trust Senior Clinical Fellowship (091673/Z/10/Z), and his research is supported by the Alzheimer’s Society, Alzheimer’s Research UK and the NIHR UCLH Biomedical Research Centre. SO is funded by the Engineering and Physical Sciences Research Council (EP/H046410/1, EP/J020990/1, EP/K005278), the Medical Research Council (MR/J01107X/1), the EU-FP7 project VPH-DARE@IT (FP7- ICT-2011-9- 601055) and the National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative BW.mn.BRC10269). JEI is supported by the European Research Council (Starting Grant 677697, project BUNGEE-TOOLS). MAS acknowledges the financial support by the EPSRC-funded UCL Centre for Doctoral Training in Medical Imaging (EP/L016478/1).

Availability of data and materials

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Authors’ contributions

MB drafted the body of the manuscript, tables and figures and ran the analyses. JDR contributed to the design and concept of the study. JEI contributed to the data analyses. LLR, CVG, CRM, JDW and JDR were responsible for the collection of data and recruitment of patients. All authors critically reviewed and approved the final manuscript and contributed to the data interpretation.

Ethics approval and consent to participate

This study was approved by the London Queen Square NRES Committee. Written informed consent was obtained from all participants.

Consent for publication

Not applicable.

Competing interests

JDR has been on a Medical Advisory Board for Wave Life Sciences and Ionis Pharmaceuticals. All other authors declare that they have no competing interests.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, 8-11 Queen Square, London, WC1N 3BG, UK
(2)
Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
(3)
School of Biomedical Engineering and Imaging Sciences, St Thomas’ Hospital, King’s College London, London, UK

References

  1. Rohrer JD, Warren JD, Modat M, Ridgway GR, Douiri A, Rossor MN, Ourselin S, Fox NC. Patterns of cortical thinning in the language variants of frontotemporal lobar degeneration. Neurology. 2009;72(18):1562–9.View ArticleGoogle Scholar
  2. Rohrer JD, Rosen HJ. Neuroimaging in frontotemporal dementia. Int Rev Psychiatry. 2013;25(2):221–9.View ArticleGoogle Scholar
  3. Schroeter ML, Raczka K, Neumann J, Yves von Cramon D. Towards a nosology for frontotemporal lobar degenerations-a meta-analysis involving 267 subjects. Neuroimage. 2007;36(3):497–510.View ArticleGoogle Scholar
  4. Rohrer JD, McNaught E, Foster J, Clegg SL, Barnes J, Omar R, Warrington EK, Rossor MN, Warren JD, Fox NC. Tracking progression in frontotemporal lobar degeneration: serial MRI in semantic dementia. Neurology. 2008;71(18):1445–51.View ArticleGoogle Scholar
  5. Lehmann M, Douiri A, Kim LG, Modat M, Chan D, Ourselin S, Barnes J, Fox NC. Atrophy patterns in Alzheimer’s disease and semantic dementia: a comparison of FreeSurfer and manual volumetric measurements. Neuroimage. 2010;49(3):2264–74.View ArticleGoogle Scholar
  6. Nestor PJ, Fryer TD, Hodges JR. Declarative memory impairments in Alzheimer’s disease and semantic dementia. Neuroimage. 2006;30(3):1010–20.View ArticleGoogle Scholar
  7. Gorno-Tempini ML, Hillis AE, Weintraub S, Kertesz A, Mendez M, Cappa SF, Ogar JM, Rohrer JD, Black S, Boeve BF, Manes F, Dronkers NF, Vandenberghe R, Rascovsky K, Patterson K, Miller BL, Knopman DS, Hodges JR, Mesulam MM, Grossman M. Classification of primary progressive aphasia and its variants. Neurology. 2011;76(11):1006–14.View ArticleGoogle Scholar
  8. Dunn DM, Dunn LM, National Foundation for Educational Research in England and Wales, GL Assessment (Firm). 2009. 3rd ed. GL Assessment. ISBN-10: 0708719554.Google Scholar
  9. Wear HJ, Wedderburn CJ, Mioshi E, Williams-Gray CH, Mason SL, Barker RA, Hodges JR. The Cambridge Behavioural Inventory revised. Dement Neuropsychol. 2008;2(2):102–7.View ArticleGoogle Scholar
  10. Cardoso MJ, Modat M, Wolz R, Melbourne A, Cash D, Rueckert D, Ourselin S. Geodesic information flows: spatially-variant graphs and their application to segmentation and fusion. IEEE TMI. 2015. https://doi.org/10.1109/TMI.2015.2418298.View ArticleGoogle Scholar
  11. Saygin ZM, Kliemann D, Iglesias JE, van der Kouwe AJW, Boyd E, Reuter M, Stevens A, Van Leemput K, McKee A, Frosch MP, Fischl B, Augustinack JC, Alzheimer’s Disease Neuroimaging Initiative. High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas. Neuroimage. 2017;155:370–82.View ArticleGoogle Scholar
  12. Iglesias JE, Augustinack JC, Nguyen K, Player CM, Player A, Wright M, Roy N, Frosch MP, McKee AC, Wald LL, Fischl B, Van Leemput K. A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: application to adaptive segmentation of in vivo MRI. Neuroimage. 2015;115:117–37.View ArticleGoogle Scholar
  13. deCampo DM, Fudge JL. Where and what is the paralaminar nucleus? A review on a unique and frequently overlooked area of the primate amygdala. Neurosci Biobehav Rev. 2012;36(1):520–35.View ArticleGoogle Scholar
  14. Malone IB, Leung KK, Clegg S, Barnes J, Whitwell JL, Ashburner J, Fox NC, Ridgway GR. Accurate automatic estimation of total intracranial volume: a nuisance variable with less nuisance. Neuroimage. 2015;104:366–72.View ArticleGoogle Scholar
  15. Czarnecki K, Duffy JR, Nehl CR, Cross SA, Molano JR, Jack CR Jr, Shiung MM, Josephs KA, Boeve BF. Very early semantic dementia with progressive temporal lobe atrophy: an 8-year longitudinal study. Arch Neurol. 2008;65(12):1659–63.View ArticleGoogle Scholar
  16. LeDoux J. The amygdala. Curr Biol. 2007;17(20):R868–74.View ArticleGoogle Scholar
  17. Rosen HJ, Perry RJ, Murphy J, Kramer JH, Mychack P, Schuff N, Weiner M, Levenson RW, Miller BL. Emotion comprehension in the temporal variant of frontotemporal dementia. Brain. 2002;125(Pt 10):2286–95.View ArticleGoogle Scholar
  18. Snowden JS, Harris JM, Thompson JC, Kobylecki C, Jones M, Richardson AM, Neary D. Semantic dementia and the left and right temporal lobes. Cortex. 2017. https://doi.org/10.1016/j.cortex.2017.08.024.View ArticleGoogle Scholar
  19. Sollberger M, Rosen HJ, Shany-Ur T, Ullah J, Stanley CM, Laluz V, Weiner MW, Wilson SM, Miller BL, Rankin KP. Neural substrates of socioemotional self-awareness in neurodegenerative disease. Brain Behav. 2014;4(2):201–14.View ArticleGoogle Scholar
  20. La Joie R, Perrotin A, de La Sayette V, Egret S, Doeuvre L, Belliard S, Eustache F, Desgranges B, Chételat G. Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer’s disease and semantic dementia. Neuroimage Clin. 2013;3:155–62.View ArticleGoogle Scholar
  21. Tan RH, Wong S, Kril JJ, Piguet O, Hornberger M, Hodges JR, Halliday GM. Beyond the temporal pole: limbic memory circuit in the semantic variant of primary progressive aphasia. Brain. 2014;137(Pt 7:2065–76.View ArticleGoogle Scholar
  22. Fletcher PD, Warren JD. Semantic dementia: a specific network-opathy. J Mol Neurosci. 2011;45(3):629–36.View ArticleGoogle Scholar
  23. de Flores R, Mutlu J, Bejanin A, Gonneaud J, Landeau B, Tomadesso C, Mézenge F, de La Sayette V, Eustache F, Chételat G. Intrinsic connectivity of hippocampal subfields in normal elderly and mild cognitive impairment patients. Hum Brain Mapp. 2017;38(10):4922–32.View ArticleGoogle Scholar
  24. Herten A, Konrad K, Krinzinger H, Seitz J, von Polier GG. Accuracy and bias of automatic hippocampal segmentation in children and adolescents. Brain Struct Funct. 2018. https://doi.org/10.1007/s00429-018-1802-2.View ArticleGoogle Scholar
  25. Whelan CD, Hibar DP, van Velzen LS, Zannas AS, Carrillo-Roa T, McMahon K, Prasad G, Kelly S, Faskowitz J, deZubiracay G, Iglesias JE, van Erp TGM, Frodl T, Martin NG, Wright MJ, Jahanshad N, Schmaal L, Sämann PG, Thompson PM, Alzheimer’s Disease Neuroimaging Initiative. Heritability and reliability of automatically segmented human hippocampal formation subregions. Neuroimage. 2016;128:125–37.View ArticleGoogle Scholar

Copyright

© The Author(s). 2019

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